TWI799092B - Identification method of identification system - Google Patents
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本發明係有關於一種辨識系統,特別係關於一種辨識系統及其方法。The present invention relates to an identification system, in particular to an identification system and its method.
指紋辨識技術發展至今,已成為多數智慧型手機的標準配備,指紋辨識的優點在於指紋是人體獨一無二的特徵,並且指紋的複雜度足夠用於鑒別,此外當需要增加可靠性時,只需登記更多的指紋並且鑒別更多的手指,最多可以多達十個,而每一個指紋都是獨一無二的。再者,現今掃描指紋的速度相當快且使用方便,亦是指紋辨識技術能夠占領大部分市場的一個主要原因。Since the development of fingerprint identification technology, it has become the standard equipment of most smart phones. The advantage of fingerprint identification is that fingerprints are unique characteristics of the human body, and the complexity of fingerprints is sufficient for identification. Multiple fingerprints and identify more fingers, up to ten, each unique. Furthermore, the speed of scanning fingerprints is quite fast and easy to use, which is also a major reason why fingerprint recognition technology can occupy most of the market.
然而,指紋辨識並非絕對安全,人們每天會將指紋留在許多公開造訪的場合,若真的有人有心想取獲、複製,那取得指紋資訊是非常輕而易舉的舉動。一旦指紋被還原採用,個人的裝置、資訊安全都有可能遭到竊取。同時,相較於使用密碼,即便密碼遭受破解後仍然可以重新設定,而指紋卻無法重設。有鑑於此,如何增進指紋辨識的安全性及辨識能力則為研發人員應解決的問題之一。However, fingerprint identification is not absolutely safe. People leave their fingerprints in many public places every day. If someone really wants to obtain and copy them, it is very easy to obtain fingerprint information. Once the fingerprint is restored, personal devices and information security may be stolen. At the same time, compared with using a password, even if the password is cracked, it can still be reset, but the fingerprint cannot be reset. In view of this, how to improve the security and identification ability of fingerprint identification is one of the problems that researchers should solve.
此外,指紋辨識技術中的辨識能力指標係為重要指標,辨識能力指標代表的是評估或比較生物辨識安全系統之性能的指標,其中包含接受誤差率(False Acceptance Rate, FAR)以及拒絕誤差率(False Rejection Rate, FRR)。需要進一步說明的是,接受誤差率表示生物辨識系統誤將不合法使用者辨認為合法使用者的機率,亦即生物辨識系統的安全程度;拒絕誤差率表示生物辨識系統將合法使用者誤判為不合法使用者的機率,亦即生物辨識系統的便利程度。因此,如何使得接受誤差率降低並且使得拒絕誤差率提升係為研發人員待解決的課題之一。In addition, the identification ability index in fingerprint identification technology is an important index. The identification ability index represents an index for evaluating or comparing the performance of biometric security systems, including acceptance error rate (False Acceptance Rate, FAR) and rejection error rate ( False Rejection Rate, FRR). It needs to be further explained that the acceptance error rate indicates the probability that the biometric system mistakenly identifies an illegal user as a legitimate user, that is, the security level of the biometric system; the rejection error rate indicates that the biometric system misjudges a legitimate user as an unauthorized user. The probability of legitimate users, that is, the convenience of the biometric system. Therefore, how to reduce the acceptance error rate and increase the rejection error rate is one of the issues to be solved by the researchers.
是以,本案發明人在觀察上述缺失後,而遂有本發明之產生。Therefore, the inventor of this case came up with the present invention after observing the above-mentioned deficiency.
本發明的目的在於提供一種辨識方法,其係藉由影像感測器針對待測物於時間範圍內產生動態影像,動態影像包含有複數區間影像,其中,時間範圍包含複數曝光區間,並且當時間範圍越靠後時該等區間影像越加清晰,使得影像感測器係進一步產生透視影像,根據該動態影像是否具有一變化過程以及該透視影像以判斷動態影像是否為生物影像,如此一來,有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統,並且大幅增加辨識系統的安全性及辨識能力。The purpose of the present invention is to provide an identification method, which uses an image sensor to generate a dynamic image within a time range for the object to be tested. The dynamic image includes a plurality of interval images, wherein the time range includes a plurality of exposure intervals, and when the time The images in these intervals are clearer when the range is further back, so that the image sensor system can further generate a perspective image, and judge whether the dynamic image is a biological image according to whether the dynamic image has a change process and the perspective image. In this way, Effectively prevent others from cracking the identification system with fingerprint images, pictures, or arbitrary models, and greatly increase the security and identification capabilities of the identification system.
本發明的又一目的在於提供一種辨識方法,其係藉由處理模組執行演算法,該演算法將不同曝光區間的動態影像相互執行相減運算,使得根據本發明之動態影像的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。藉此,大幅增進根據本發明之辨識系統的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。Another object of the present invention is to provide an identification method, which uses a processing module to execute an algorithm, which subtracts dynamic images in different exposure intervals from each other, so that the RV value of the dynamic image according to the present invention ( ridge valley value) has been greatly improved, resulting in better fingerprint recognition results. Thereby, the Accepted Error Rate (FAR) and Rejected Error Rate (FRR) of the identification system according to the present invention are greatly improved, achieving the goals of high accuracy and wide applicability.
為達上述目的,本發明提供一種辨識方法,其係應用於一辨識系統,該辨識系統包含一感測區域、一影像感測器以及一辨識模組,該辨識模組耦接於該感測區域以及該影像感測器,該辨識方法包含下列步驟:一啟動步驟,當該待測物接觸該感測區域時,該影像感測器啟動並針對該待測物產生一動態影像;一感測步驟,該待測物從接觸至覆蓋該感測區域,使得該影像感測器進一步產生一透視影像;以及一辨識步驟,若該待測物的具有該動態影像之一變化過程以及該透視影像,則該辨識模組判斷該動態影像為生物影像,反之,則該辨識模組判斷該動態影像為非生物影像。To achieve the above purpose, the present invention provides an identification method, which is applied to an identification system, the identification system includes a sensing area, an image sensor and an identification module, the identification module is coupled to the sensing area and the image sensor, the identification method includes the following steps: a starting step, when the object to be tested touches the sensing area, the image sensor is activated and generates a dynamic image for the object to be tested; a sensing A detection step, the object under test covers the sensing area from contact, so that the image sensor further generates a perspective image; and an identification step, if the object under test has a change process of the dynamic image and the perspective image, the identification module determines that the dynamic image is a biological image; otherwise, the identification module determines that the dynamic image is a non-biological image.
較佳地,根據本發明之辨識方法,其中,該動態影像包含該待測物於一時間範圍內產生之複數區間影像,該辨識模組根據該等區間影像之清晰值是否超過一閾值判斷該動態影像是否具有該變化過程,當該等清晰值其中之一者超過該閾值時,則該辨識模組判斷該動態影像具有該變化過程。Preferably, according to the identification method of the present invention, wherein the dynamic image includes a plurality of interval images generated by the object under test within a time range, the identification module judges the Whether the dynamic image has the change process, when one of the sharpness values exceeds the threshold, the identification module determines that the dynamic image has the change process.
較佳地,根據本發明之辨識方法,其中,該清晰值係透過影像差異值法和影像梯度值其中之一者進行計算。Preferably, in the identification method according to the present invention, the sharpness value is calculated through one of the image difference method and the image gradient value.
較佳地,根據本發明之辨識方法,其中,該影像感測器係進一步包含:複數光感測器,其係呈陣列排列,該等光感測器係用於產生複數影像強度資訊,並且藉由該等影像強度資訊產生該動態影像;複數互補性氧化金屬半導體 (Complementary Metal-Oxide Semiconductor,CMOS),其係耦接於該等光感測器,該等互補性氧化金屬半導體係用於控制該等影像強度資訊的輸出,然而本發明不限於此。Preferably, according to the identification method of the present invention, the image sensor further includes: a plurality of photosensors arranged in an array, the photosensors are used to generate complex image intensity information, and The dynamic image is generated by the image intensity information; a plurality of complementary metal-oxide semiconductors (Complementary Metal-Oxide Semiconductor, CMOS), which are coupled to the light sensors, and the complementary metal-oxide semiconductors are used for The output of the image intensity information is controlled, but the present invention is not limited thereto.
較佳地,根據本發明之辨識方法,其中,該影像感測器係設置於該感測區域下方,該影像感測器具有一快門機制,該快門機制係用於控制該曝光區間,然而本發明不限於此。Preferably, according to the identification method of the present invention, wherein, the image sensor is arranged below the sensing area, the image sensor has a shutter mechanism, and the shutter mechanism is used to control the exposure interval, but the present invention Not limited to this.
較佳地,根據本發明之辨識方法,其中,該快門機制係為全局式快門(Global Shutter, GS),以使該等光感測器同時曝光產生該等影像強度資訊,然而本發明不限於此。Preferably, according to the identification method of the present invention, the shutter mechanism is a global shutter (Global Shutter, GS), so that the photosensors are simultaneously exposed to generate the image intensity information, but the present invention is not limited to this.
又,為達上述目的,本發明係根據上述辨識系統為基礎,進一步提供一種執行上述辨識系統的辨識方法,其係包含有:一待測物接近該感測區域,當該待測物接觸該感測區域時,該影像感測器啟動並針對該待測物產生一動態影像,該動態影像包含複數區間影像;一相減步驟,一處理模組執行一演算法以將該等區間影像相互執行相減運算,並且產生複數相減訊號;一訊號放大步驟,一運算模組以倍數放大該等相減訊號,使放大後的該等相減訊號之波峰及波谷清晰且在訊號處理範圍內;以及一辨識步驟,一辨識模組根據放大後的該等相減訊號作為該辨識系統是否解鎖之依據。Moreover, in order to achieve the above object, the present invention is based on the above identification system, and further provides an identification method for implementing the above identification system, which includes: an object under test approaches the sensing area, when the object under test touches the sensing area, When sensing the area, the image sensor is activated and generates a dynamic image for the object under test, the dynamic image includes a plurality of interval images; a subtraction step, a processing module executes an algorithm to compare the interval images Perform subtraction operations and generate complex subtraction signals; a signal amplification step, an operation module amplifies the subtraction signals by multiples, so that the peaks and valleys of the amplified subtraction signals are clear and within the signal processing range and an identification step, where an identification module uses the amplified subtraction signals as the basis for whether the identification system is unlocked.
較佳地,根據本發明之辨識方法,其中,在執行該訊號放大步驟後該辨識方法係進一步包括:一平均步驟,該運算模組將該等相減訊號取一平均值;其中,該辨識步驟進一步透過該辨識模組根據該平均值作為該辨識系統是否解鎖之依據。Preferably, according to the identification method of the present invention, wherein, after performing the signal amplification step, the identification method further includes: an averaging step, the calculation module taking an average value of the subtracted signals; wherein, the identification The step further uses the identification module as a basis for unlocking the identification system according to the average value.
較佳地,根據本發明之辨識方法進一步包括一選定步驟,該處理模組將該等區間影像中的其中之一者作為一背景區間影像;其中,該相減步驟進一步將該背景區間影像與該等區間影像執行相減運算,並且產生該等相減訊號。Preferably, the identification method according to the present invention further includes a selection step, the processing module uses one of the interval images as a background interval image; wherein, the subtraction step further combines the background interval image with A subtraction operation is performed on the interval images, and the subtraction signals are generated.
綜上,本發明所提供之辨識方法,主要利用本發明之影像感測器針對待測物於時間範圍內產生動態影像,其中,時間範圍包含複數曝光區間,並且當曝光區間越久該動態影像之區間影像的清晰值超過閾值,並且影像感測器係進一步產生透視影像,以根據該動態影像是否具有該變化過程以及該透視影像以判斷動態影像是否為生物影像,如此一來,有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統,並且大幅增加辨識系統的安全性及辨識能力。此外,藉由處理模組執行演算法,該演算法將不同曝光區間的動態影像相互執行相減運算後取平均值,使得根據本發明之動態影像的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。藉此,大幅增進根據本發明之辨識系統的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。To sum up, the identification method provided by the present invention mainly utilizes the image sensor of the present invention to generate a dynamic image for the object under test within a time range, wherein the time range includes multiple exposure intervals, and the longer the exposure interval, the longer the The clarity value of the interval image exceeds the threshold, and the image sensor further generates a perspective image to judge whether the dynamic image is a biological image according to whether the dynamic image has the change process and the perspective image. In this way, it is effective to prevent others from using Fingerprint images, pictures, or arbitrary models crack the identification system, and greatly increase the security and identification capabilities of the identification system. In addition, through the processing module to execute the algorithm, the algorithm subtracts the dynamic images of different exposure intervals from each other and then takes the average value, so that the RV value (ridge valley value) of the dynamic image according to the present invention is greatly improved, resulting in Better fingerprint recognition effect. Thereby, the Accepted Error Rate (FAR) and Rejected Error Rate (FRR) of the identification system according to the present invention are greatly improved, achieving the goals of high accuracy and wide applicability.
爲使熟悉該項技藝人士瞭解本發明之目的、特徵及功效,茲藉由下述具體實施例,並配合所附之圖式,對本發明詳加說明如下。In order to enable those skilled in the art to understand the purpose, features and effects of the present invention, the present invention will be described in detail below by means of the following specific embodiments and accompanying drawings.
現在將參照其中示出本發明概念的示例性實施例的附圖 在下文中更充分地闡述本發明概念。以下藉由參照附圖更詳細地闡述的示例性實施例,本發明概念的優點及特徵以及其達成方法將顯而易見。然而,應注意,本發明概念並非僅限於以下示例性實施例,而是可實施為各種形式。因此,提供示例性實施例僅是為了揭露本發明概念並使熟習此項技術者瞭解本發明概念的類別。在圖式中,本發明概念的示例性實施例並非僅限於本文所提供的特定實例且為清晰起見而進行誇大。The inventive concept will now be explained more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the inventive concept are shown. Advantages and features of the inventive concept and methods for achieving it will be apparent below by referring to the exemplary embodiments described in more detail with reference to the accompanying drawings. It should be noted, however, that the inventive concept is not limited to the following exemplary embodiments, but can be implemented in various forms. Therefore, the exemplary embodiments are provided only to disclose the inventive concept and to make one skilled in the art understand the category of the inventive concept. In the drawings, the exemplary embodiments of the inventive concepts are not limited to the specific examples provided herein and are exaggerated for clarity.
本文所用術語僅用於闡述特定實施例,而並非旨在限制本發明。除非上下文中清楚地另外指明,否則本文所用的單數形式的用語「一」及「該」旨在亦包括複數形式。本文所用的用語「及/或」包括相關所列項其中一或多者的任意及所有組合。應理解,當稱元件「連接」或「耦合」至另一元件時,所述元件可直接連接或耦合至所述另一元件或可存在中間元件。The terminology used herein is for describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the terms "a", "an" and "the" in the singular are intended to include the plural forms as well, unless the context clearly dictates otherwise. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present.
相似地,應理解,當稱一個元件(例如層、區或基板)位於另一元件「上」時,所述元件可直接位於所述另一元件上,或可存在中間元件。相比之下,用語「直接」意指不存在中間元件。更應理解,當在本文中使用用語「包括」、「包含」時,是表明所陳述的特徵、整數、步驟、操作、元件、及/或組件的存在,但不排除一或多個其他特徵、整數、步驟、操作、元件、組件、及/或其群組的存在或添加。Similarly, it will be understood that when an element such as a layer, region or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may be present. In contrast, the term "directly" means that there are no intervening elements. It should be further understood that when the words "comprising" and "comprising" are used herein, it indicates the existence of stated features, integers, steps, operations, elements, and/or components, but does not exclude one or more other features. , integers, steps, operations, elements, components, and/or the presence or addition of groups thereof.
此外,將藉由作為本發明概念的理想化示例性圖的剖視圖來闡述詳細說明中的示例性實施例。相應地,可根據製造技術及/或可容許的誤差來修改示例性圖的形狀。因此,本發明概念的示例性實施例並非僅限於示例性圖中所示出的特定形狀,而是可包括可根據製造製程而產生的其他形狀。圖式中所例示的區域具有一般特性,且用於說明元件的特定形狀。因此,此不應被視為僅限於本發明概念的範圍。Furthermore, exemplary embodiments in the detailed description will be explained by means of cross-sectional views that are idealized exemplary views of the inventive concept. Accordingly, the shapes of the exemplary figures may be modified according to manufacturing techniques and/or allowable errors. Accordingly, exemplary embodiments of the inventive concepts are not limited to the specific shapes shown in the exemplary figures, but may include other shapes that may be produced according to manufacturing processes. Regions illustrated in the drawings have general characteristics and are used to illustrate specific shapes of elements. Accordingly, this should not be seen as limiting the scope of the inventive concept.
亦應理解,儘管本文中可能使用用語「第一」、「第二」、「第三」等來闡述各種元件,然而該些元件不應受限於該些用語。該些用語僅用於區分各個元件。因此,某些實施例中的第一元件可在其他實施例中被稱為第二元件,而此並不背離本發明的教示內容。本文中所闡釋及說明的本發明概念的態樣的示例性實施例包括其互補對應物。本說明書通篇中,相同的參考編號或相同的指示物表示相同的元件。It should also be understood that although the terms “first”, “second”, “third” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish various elements. Thus, a first element in some embodiments could be termed a second element in other embodiments without departing from the teachings of the present invention. Exemplary embodiments of aspects of the inventive concept illustrated and illustrated herein include their complementary counterparts. Throughout this specification, the same reference number or the same designator designates the same element.
此外,本文中參照剖視圖及/或平面圖來闡述示例性實施例,其中所述剖視圖及/或平面圖是理想化示例性說明圖。因此,預期存在由例如製造技術及/或容差所造成的相對於圖示形狀的偏離。因此,示例性實施例不應被視作僅限於本文中所示區的形狀,而是欲包括由例如製造所導致的形狀偏差。因此,圖中所示的區為示意性的,且其形狀並非旨在說明裝置的區的實際形狀、亦並非旨在限制示例性實施例的範圍。Additionally, exemplary embodiments are described herein with reference to cross-sectional and/or plan views, which are idealized exemplary illustrations. Accordingly, deviations from the illustrated shapes as a result, for example, of manufacturing techniques and/or tolerances are to be expected. Thus, example embodiments should not be construed as limited to the shapes of regions illustrated herein but are to include deviations in shapes that result, for example, from manufacturing. Thus, the regions shown in the figures are schematic and their shapes are not intended to illustrate the actual shape of a region of a device and are not intended to limit the scope of example embodiments.
請參閱圖1至圖4所示,圖1為根據本發明之辨識系統的示意圖;圖2為說明執行本發明之辨識方法的步驟方塊圖;圖3為說明根據本發明之辨識方法實際執行過程之步驟流程圖。如圖1所示,根據本發明之辨識系統100包括:感測區域11、影像感測器12、以及辨識模組13。Please refer to Fig. 1 to Fig. 4, Fig. 1 is a schematic diagram according to the identification system of the present invention; Fig. 2 is a block diagram illustrating the steps of implementing the identification method of the present invention; Fig. 3 is an illustration of the actual execution process of the identification method according to the present invention The flow chart of the steps. As shown in FIG. 1 , an
具體地,根據本發明之感測區域11,其係用於供待測物200接近以執行感測。在一些實施例中,感測區域11可以作為辨識系統100的隔離結構。在本發明中,用詞「隔離」涵蓋電性隔離及物理隔離二個方面。辨識系統100可以為無機封裝材料的單層、無機封裝材料的多層堆疊、或成對的無機封裝材料與有機封裝材料的堆疊。所使用的無機封裝材料例如但不限於為氮化矽(SiNx)、氧化矽(SiOx)、氮氧化矽(SiONx)、氧化鋁(AlOx)、或氧化鈦(TiOx),然而本發明不限於此。Specifically, according to the
具體地,根據本發明之影像感測器12,其係設置於感測區域11下方,影像感測器12具有快門機制(圖未示),影像感測器12係用於針對待測物200於時間範圍31內產生動態影像21,其中動態影像21包含複數區間影像23(圖4中所示的23-1至23-8),該時間範圍31包含複數曝光區間32,該快門機制係用於控制該曝光區間32,並且曝光區間32與區間影像23相互對應。在一些實施例中,根據本發明之影像感測器12可以是互補性氧化金屬半導體 (Complementary Metal-Oxide Semiconductor,CMOS)影像感測器,並且可以式選自背照式CMOS影像感測器或前照式CMOS影像感測器其中之一者,然而本發明不限於此。Specifically, the
需要進一步說明的是,在一些實施例中,根據本發明之影像感測器12可以具有滾動式快門(Rolling Shutter)機制與全域式快門(Global Shutter)機制其中之一者,其中,使用滾動式快門機制時,因滾動式快門機制曝光有時間差異,造成於影像感測器12拍攝動態的影像時,區間影像23上下半部分的曝光時間不同,可能造成區間影像23之上半部分先出現,然而區間影像23之下端卻尚未出現的時間差距,從而造成區間影像23之影像扭曲變形。在本發明一較佳實施例中,由於本發明係主要針對待測物200於時間範圍31內產生動態影像21,為避免動態影像21之區間影像23產生果凍效應(Jello Effect)因此採用全域式快門機制,亦即影像感測器12上的所有畫素陣列的每個畫素都會在同一曝光區間32同時取得區間影像23,然而本發明不限於此。It should be further explained that, in some embodiments, the
具體地,根據本發明之辨識模組13,其係耦接於影像感測器12,並且辨識模組13係根據透視影像22判斷動態影像21是否為生物影像。需要進一步說明的是,在一些實施例中,待測物200係可以為人體之指紋,並且透視影像22係可以為人體之靜脈血管或者與人體之靜脈血管相關聯,並且辨識模組13係可以藉由影像感測器12拍攝手指靜脈血管之透視影像22,以將透視影像22作為判斷動態影像21是否為生物影像之依據,然而本發明不限於此。在本發明中,用詞「生物影像」代表的是防止他人以指紋之影像、圖片、或任意模型破解辨識系統100。Specifically, the
值得一提的是,在一些實施例中,根據本發明之辨識系統100係可以執行演算法(圖未示),以將不同曝光區間32的區間影像23相互執行相減運算,使得根據本發明之動態影像21的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果,又,在本文中所述之相減運算,可以是指透過減去該等區間影像23中的其中之一者後減少雜訊值的運算方式。在本發明一較佳實施例中,相減運算係為後一曝光區間32的區間影像23減去前一曝光區間32的區間影像23,並且在執行完相減運算後將所得的數值進行平均運算。可以理解的是,習知技術之指紋辨識系統,僅將不同曝光區間32的區間影像23相互執行相加運算後取平均值,或者僅扣除空無一物的背景值後即將區間影像23執行相加運算後取平均值,然而由於扣除的背景值與區間影像23之間的差距過大,造成無法有效扣除區間影像23中的雜訊,導致將區間影像23中的影像放大的同時,雜訊將一併被同步放大。綜上所述,根據本發明之辨識系統藉由執行演算法,將不同曝光區間的區間影像23相互執行相減運算,大幅增進根據本發明之辨識系統的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。It is worth mentioning that, in some embodiments, the
為供進一步瞭解本發明構造特徵、運用技術手段及所預期達成之功效,茲將本發明使用方式加以敘述,相信當可由此而對本發明有更深入且具體瞭解,如下所述:In order to further understand the structural features, technical means and expected effects of the present invention, the use of the present invention will be described hereby. It is believed that the present invention can be understood more deeply and specifically, as follows:
請參閱圖2,並且搭配圖4所示,本發明以上述之辨識系統100為基礎,進一步提供一種辨識系統100的辨識方法,係包含下列步驟:Please refer to FIG. 2, and as shown in FIG. 4, the present invention is based on the above-mentioned
啟動步驟S11,當待測物200接觸感測區域11時,影像感測器12啟動並針對待測物200產生動態影像21,動態影像21包含複數區間影像23,接著執行感測步驟S12。In the starting step S11 , when the object under
感測步驟S12,待測物200從接觸至完全覆蓋感測區域11,使得影像感測器12進一步產生透視影像22,接著執行辨識步驟S13。In the sensing step S12 , the object under
辨識步驟S13,若待測物200具有動態影像之變化過程以及透視影像22,則辨識模組13判斷待測物200之動態影像21為生物影像。In the identification step S13 , if the
值得一提的是,如圖3所示,根據本發明之辨識系統100可透過時間範圍31內不同曝光區間32的區間影像23,來對待測物200於時間範圍31內產生之動態影像21進行辨識真偽,舉例而言,偽裝的指紋可能無法如生物影像的指紋一般於時間範圍31內動態影像21逐漸變大以及清晰至可產生透視影像22,因此,若動態影像21於時間範圍31內的變化過程異常,則根據本發明之辨識系統100可以確認動態影像21並非生物,如此一來,進一步提升本發明之辨識準確率。It is worth mentioning that, as shown in FIG. 3 , the
需要進一步說明的是,在一些實施例中,辨識模組13係進一步根據變化過程中的動態影像21之區間影像23之清晰值(圖未示)是否超過一閾值(圖未示),以判斷動態影像21是否為生物影像,該清晰值係透過影像差異值法和影像梯度值其中之一者進行計算,並且該閾值可以經由使用者自行設定,或者透過各式演算法(例如:平均值計算)以及過往的區間影像23的清晰值進行運算產生。其中,影像差異值法主要透過整張區間影像23的平均值,經由整張區間影像23每個點的影像值與平均值相減取絕對值,取得相鄰像素間其差異值,最後平均求得清晰值,清晰值愈大表示影像愈清晰;另,影像梯度值主要透過離散微分運算元(discrete differentiation operator) 對區間影像23的每個影像值做垂直和水平卷積運算,所得到的影像梯度值做為清晰值,清晰值愈大表示影像愈清晰,然而本發明不限於此。It should be further explained that, in some embodiments, the
藉此,根據本發明之辨識系統100藉由影像感測器12針對待測物200於時間範圍31內產生動態影像21,動態影像21包含複數區間影像23,其中,時間範圍31包含複數曝光區間32,並且當時間範圍31越靠後時,區間影像23越清晰,使得影像感測器12係進一步產生透視影像22,並根據動態影像21的變化過程(圖未示)以及透視影像22以確認動態影像21是否為生物影像,如此一來,有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統,並且大幅增加辨識系統100的安全性及辨識能力。Thus, the
請參閱圖4-8所示,圖4為根據本發明第一實施例之辨識系統的示意圖;圖5為根據本發明第一實施例之影像感測器的示意圖;圖6為說明根據本發明第一實施例之辨識方法的步驟方塊圖;圖7為說明執行本發明第一實施例之辨識方法的又一步驟方塊圖;圖8為說明根據本發明第一實施例之辨識方法實際執行過程之步驟流程圖。如圖4所示,根據本發明之辨識系統100包括:感測區域11、影像感測器12、辨識模組13、以及處理模組14。Please refer to FIGS. 4-8. FIG. 4 is a schematic diagram of an identification system according to a first embodiment of the present invention; FIG. 5 is a schematic diagram of an image sensor according to a first embodiment of the present invention; FIG. 6 is an illustration according to the present invention The step block diagram of the identification method of the first embodiment; FIG. 7 is another step block diagram illustrating the implementation of the identification method of the first embodiment of the present invention; FIG. 8 illustrates the actual execution process of the identification method according to the first embodiment of the present invention The flow chart of the steps. As shown in FIG. 4 , the
具體地,請參閱圖4所示,根據本發明第一實施例之辨識系統100,其係進一步包含有處理模組14,其係耦接於辨識模組13,處理模組14係用於執行演算法,該演算法將區間影像23相互執行相減運算,並且產生複數相減訊號(圖未示),然而本發明不限於此。Specifically, as shown in FIG. 4, the
具體地,請參閱圖5所示,根據本發明第一實施例之影像感測器12,其係進一步包含有光感測器121以及互補性氧化金屬半導體122,其中,光感測器121係呈陣列排列,光感測器121係用於產生影像強度資訊(圖未示),並且藉由該等影像強度資訊產生動態影像21;互補性氧化金屬半導體122係耦接於光感測器,互補性氧化金屬半導體122係用於控制該等影像強度資訊的輸出。需要進一步說明的是,根據本發明第一實施例之影像感測器12係可以藉由彩色濾光片陣列(color filter array,CFA)將影像感測器12所接收到的影像轉換為紅光、綠光、以及藍光,並藉由與之相對應的光感測器121產生影像強度資訊,以獲得穩定的動態影像21品質,然而本發明不限於此。Specifically, as shown in FIG. 5, the
請參閱圖6及圖8所示,根據本發明係以第一實施例之辨識系統100為基礎,進一步提供一種執行第一實施例之辨識系統100的辨識方法,其係包含下列步驟:Please refer to FIG. 6 and FIG. 8 , according to the present invention, based on the
啟動步驟S11',當待測物200接觸感測區域11時,使得影像感測器12啟動並針對待測物200產生動態影像21,動態影像21包含複數區間影像23,接著執行感測步驟S12'。Start step S11', when the object under
感測步驟S12',待測物200從接觸至覆蓋感測區域11使得影像感測器12進一步產生透視影像22,接著執行運算步驟S13'。In the sensing step S12', the object under
運算步驟S13',處理模組14透過影像差異值法和影像梯度值其中之一者進行計算時間範圍31內的區間影像23的清晰值,接著執行辨識步驟S14'。In the operation step S13', the
辨識步驟S14',辨識模組13進一步根據區間影像23的清晰值是否超過閾值,以判斷動態影像21是否具有變化過程,若待測物200具有變化過程且具有透視影像22,則辨識模組13判斷動態影像21為生物影像,反之,則辨識模組13判斷動態影像21非生物影像。In the identification step S14', the
為供進一步瞭解本發明構造特徵、運用技術手段及所預期達成之功效,茲將本發明第一實施例實際執行過程加以敘述,相信當可由此而對本發明有更深入且具體瞭解,如下所述:In order to further understand the structural features, technical means and expected effects of the present invention, the actual implementation process of the first embodiment of the present invention will be described. It is believed that the present invention can be understood more deeply and specifically as follows. :
請參閱圖8,並且搭配圖4至圖6所示。根據本發明之辨識系統100實際執行過程說明如下:首先,執行啟動步驟S11',於曝光區間32為1時,待測物200接觸感測區域11,使得影像感測器12啟動並針對待測物200產生相對應曝光區間32為1的區間影像23-1;接著,執行感測步驟S12',於曝光區間32為3及4時,待測物200接觸至完全覆蓋感測區域11產生區間影像23-3及23-4,並且影像感測器12進一步產生透視影像22;之後,執行運算步驟S13',處理模組14透過影像差異值法和影像梯度值其中之一者進行計算時間範圍31內的區間影像23的清晰值;最後,執行辨識步驟S14',透過辨識模組13進一步根據區間影像23的清晰值是否超過閾值,以判斷動態影像21是否具有變化過程,若待測物200具有變化過程以及透視影像22,則辨識模組13判斷動態影像21為生物影像,反之,則辨識模組13判斷動態影像21非生物影像。Please refer to Figure 8 and match it with Figures 4 to 6. The actual execution process of the
藉此,由上述說明可知,根據本發明第一實施例之辨識系統100,可以藉由辨識模組13判斷動態影像21是否為生物影像,有別於習知技術使用血管靜脈辨識作為辨識辦法,辨識模組13根據動態影像21隨啟動步驟S11'至該感測步驟S12'之區間影像23的清晰值是否超過閾值,同時搭配透視影像22以判斷該動態影像21是否為生物影像,有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統100,同時辨識結果不需經過繁複的機器學習機制以及累積大量辨識特徵,使得離線辨識的可行性大幅提高,兼具廣泛適用性及高度安全性。Therefore, it can be seen from the above description that according to the
請參閱圖7,並且搭配圖8所示,在本實施例中,本發明進一步以上述第一實施例之辨識系統100為基礎,提供一種辨識系統100的辨識方法,係包含下列步驟:Please refer to FIG. 7, and as shown in FIG. 8, in this embodiment, the present invention is further based on the
啟動步驟S21,當待測物200接觸感測區域11時,影像感測器12啟動並產生動態影像21,動態影像21包含複數區間影像23,接著執行相減步驟S22。In the starting step S21 , when the object under
相減步驟S22,處理模組14執行演算法,該演算法將區間影像23執行相減運算,並且產生相減訊號(圖未示) ,接著執行辨識步驟S23。In the subtraction step S22, the
辨識步驟S23,辨識模組13根據相減訊號作為該辨識系統是否解鎖之依據。In the identification step S23, the
為供進一步瞭解本發明構造特徵、運用技術手段及所預期達成之功效,茲將本發明實際執行過程加以敘述,相信當可由此而對本發明有更深入且具體瞭解,如下所述:In order to further understand the structural features, technical means and expected effects of the present invention, the actual implementation process of the present invention will be described. It is believed that the present invention can be understood more deeply and specifically, as follows:
請參閱圖8,並且搭配圖4至圖7所示。根據本發明第一實施例之辨識系統100實際執行過程說明如下:首先,執行啟動步驟S11,於曝光區間32為1時,待測物200接觸感測區域11,使得影像感測器12啟動並產生相對應曝光區間32為5至8時的區間影像23-5至23-8;接著,執行相減步驟S22,區間影像23-8相比於之前的區間影像23-5至23-7愈加清晰並且對區間影像23執行演算法,該演算法將區間影像23相互執行相減運算,並且產生相減訊號;最後,辨識模組13根據相減訊號作為該辨識系統是否解鎖之依據。Please refer to Figure 8 and match it with Figures 4 to 7. The actual execution process of the
需要進一步說明的是,上述區間影像23可以包含有在曝光區間32內產生之複數單幀影像,且區間影像23相互執行相減運算後所產生之相減訊號可以具有複數個非僅限於單一相減訊號,本發明可以根據其中任一相減訊號作為辨識系統100是否解鎖之依據。在一些實施例中,當存在複數個相減訊號時,可以將該等相減訊號取平均值作為辨識系統100是否解鎖之依據,然而本發明不限於此。It should be further explained that the above interval image 23 may include a plurality of single-frame images generated in the
藉此,由上述說明可知,根據本發明第一實施例之辨識系統100,進一步藉由處理模組14執行演算法,該演算法將不同曝光區間32的區間影像23相互執行相減運算,使得根據本發明之動態影像的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。藉此,大幅增進根據本發明之辨識系統100的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。Therefore, it can be known from the above description that the
以下,參照圖式,說明本發明的辨識系統100的第一實施之實施形態,以使本發明所屬技術領域中具有通常知識者更清楚的理解可能的變化。以與上述相同的元件符號指示的元件實質上相同於上述參照圖1所敘述者。與辨識系統100相同的元件、特徵、和優點將不再贅述。Hereinafter, with reference to the drawings, the embodiment of the first embodiment of the
以下提供辨識系統100的其他示例,以使本發明所屬技術領域中具有通常知識者更清楚的理解可能的變化。以與上述實施例相同的元件符號指示的元件實質上相同於上述參照圖1、圖2所敘述者。與辨識系統100相同的元件、特徵、和優點將不再贅述。Other examples of the
請參閱圖9-11B所示,圖9為根據本發明第二實施例之辨識系統的示意圖;圖10為說明根據本發明第二實施例之辨識方法的步驟方塊圖;圖11A為示例性說明根據本發明第二實施例之區間影像的示意圖;圖11B為示例性說明根據本發明第二實施例之區間影像執行演算法後的示意圖。如圖9所示,根據本發明之辨識系統100包括:感測區域11、影像感測器12、辨識模組13、處理模組14、以及運算模組15。Please refer to Figures 9-11B, Figure 9 is a schematic diagram of an identification system according to a second embodiment of the present invention; Figure 10 is a block diagram illustrating the steps of an identification method according to a second embodiment of the present invention; Figure 11A is an exemplary illustration A schematic diagram of an interval image according to the second embodiment of the present invention; FIG. 11B is a schematic diagram illustrating an algorithm performed on an interval image according to the second embodiment of the present invention. As shown in FIG. 9 , the
具體地,根據本發明第二實施例之辨識系統100,其係進一步包含有運算模組15,根據本發明第二實施例之運算模組15,其係針對上述之相減訊號進行倍數放大,以保證放大後的相減訊號之波峰及波谷清晰且在訊號處理範圍內,運算模組15可以是具有運算功能之硬體或者軟體其中之一者,然而本發明不限於此。Specifically, the
需要進一步說明的是,根據本發明第二實施例之處理模組14,其係執行演算法以將時間範圍31內不同曝光區間32的區間影像23相互執行相減運算,演算法係為將時間範圍31內後一曝光區間32的區間影像23減去前一曝光區間31的動態影像後取平均值,如此一來,有別於習知技術僅將不同曝光區間32的區間影像23相互執行相加運算取平均值,或者僅扣除空無一物的背景值後即將區間影像23執行相加運算取平均值。根據本發明之辨識系統100能有效將動態影像21中的雜訊消除,使得動態影像21的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。It should be further explained that, according to the
值得一提的是,由於處理模組14與運算模組15皆用於處理區間影像23,在一些實施例中,可以進行區間影像23相互執行相減運算之演算法的產品通常也兼具相減訊號倍數放大的功能,因此處理模組14和運算模組15將可以合併為同一個角色,然而本發明不限於此。It is worth mentioning that since both the
請參閱圖10所示,根據本發明係以第二實施例之辨識系統100為基礎,進一步提供一種執行第二實施例之辨識系統100的辨識方法,其係包含下列步驟:Please refer to FIG. 10 , according to the present invention, based on the
啟動步驟S21',當待測物200接觸感測區域11時,影像感測器12啟動並產生動態影像21,動態影像21包含複數區間影像23,接著執行相減步驟S22'。In the starting step S21 ′, when the object under
相減步驟S22',透過處理模組14執行演算法,該演算法將區間影像23執行相減運算,並且產生相減訊號,接著執行訊號放大步驟S23'。In the subtraction step S22', the
訊號放大步驟S23',透過運算模組15以倍數放大相減訊號,放大後的相減訊號之波峰及波谷清晰且在訊號處理範圍內,接著執行平均步驟S24'。In the signal amplification step S23', the subtraction signal is amplified by the
平均步驟S24',透過運算模組15將相減訊號取平均值,接著執行辨識步驟S25'。In the averaging step S24', the subtraction signal is averaged through the
辨識步驟S25',辨識模組13根據放大後的相減訊號取平均值作為該辨識系統是否解鎖之依據。In the identification step S25', the
具體地,請參閱圖11A及圖11B所示,並且搭配圖8至圖10所示。根據本發明第二實施例之辨識系統100實際執行演算法過程說明如下:如圖11A所示,圖11A為示例性說明區間影像23-0、23-4至23-8在曝光區間32為60微秒的情況下的強度,,需要進一步說明的是,區間影像23-0為影像感測器12上未存在任何物體所產生的影像強度資訊,亦即習知技術中的背景值。如圖11B所示,圖11B為示例性說明執行根據本發明第二實施例之演算法後的區間影像23(23-4至23-8),以及扣除空無一物的背景值後的區間影像23-0相互比較,可以理解的是,由於習知技術之指紋辨識系統僅扣除空無一物的背景值(即23-8減23-0),使得扣除的背景值與區間影像23之間的差距過大,造成無法有效扣除區間影像23中的雜訊,影像強度資訊並無明顯的下降,反觀,使用本發明第二實施例之演算法,將時間範圍31內後一曝光區間32的區間影像23減去前一曝光區間32的區間影像23(如圖11B中所示之23-5減23-4、23-7減23-6、23-8減23-7),由於有效消除區間影像23中的雜訊,造成影像強度資訊產生明顯的下降,使得根據本發明之動態影像21的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。藉此,大幅增進根據本發明之辨識系統100的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。Specifically, please refer to FIG. 11A and FIG. 11B , together with those shown in FIG. 8 to FIG. 10 . According to the second embodiment of the present invention, the actual execution algorithm process of the
請參閱圖12-13B所示,圖12為說明根據本發明第三實施例之辨識方法的步驟方塊圖;圖13A為示例性說明根據本發明第三實施例之動態影像執行演算法後的示意圖;圖13B為示例性說明根據本發明第三實施例之動態影像執行演算法後的又一示意圖。請參閱圖12所示,根據本發明係以上述之辨識系統100為基礎,進一步提供一種執行第三實施例之辨識系統100的辨識方法,其係包含下列步驟:Please refer to Figures 12-13B, Figure 12 is a block diagram illustrating the steps of the identification method according to the third embodiment of the present invention; Figure 13A is a schematic diagram illustrating the algorithm after the dynamic image is executed according to the third embodiment of the present invention ; FIG. 13B is another schematic diagram illustrating the execution algorithm of the dynamic image according to the third embodiment of the present invention. Please refer to FIG. 12 , according to the present invention, based on the
啟動步驟S21'',當待測物200接觸該感測區域11時,使得影像感測器12啟動並產生動態影像21,動態影像21包含複數區間影像23,接著執行選定步驟S22''。In the starting step S21 ″, when the object under
選定步驟S22'',處理模組14透過演算法將區間影像23中的其中之一作為背景區間影像(圖未示),接著執行比相減步驟S23''。Step S22 ″ is selected, and the
相減步驟S23'',透過處理模組14執行演算法,該演算法進一步將該背景區間影像與區間影像23執行相減運算,並且產生相減訊號,接著執行訊號放大步驟S24''。In the subtraction step S23 ″, the
訊號放大步驟S24'',運算模組15以倍數放大相減訊號,放大後的相減訊號之波峰及波谷清晰且在訊號處理範圍內,接著執行平均步驟S25''。In the signal amplification step S24'', the
平均步驟S25'',透過運算模組15將相減訊號取平均值,接著執行辨識步驟S26''。In the averaging step S25'', the subtraction signal is averaged through the
辨識步驟S25'',透過辨識模組13根據放大後的相減訊號作為辨識系統100是否解鎖之依據。In the identification step S25 ″, the
具體地,請參閱圖13A及圖13B所示,並且搭配圖12所示。根據本發明第三實施例之辨識系統100實際執行演算法過程說明如下:如圖13A所示,圖13A為示例性說明根據本發明第三實施例之演算法分別選擇不同的區間影像23作為背景區間影像,在曝光區間32為60微秒的情況下,將區間影像23扣除不同的背景區間影像相互比較,可以理解的是,相較於習知技術扣除空無一物的背景值後的區間影像23(即23-8減23-0),當透過選定步驟S22''選擇較佳的區間影像23中的其中之一作為背景區間影像,可以有效消除區間影像23中的雜訊,造成影像強度資訊產生明顯的下降。值得一提的是,根據本發明之演算法可以透過機器學習演算法或深度學習演算法自我學習,自動判別選擇較佳的區間影像23中的其中之一作為背景動態影像,演算法可為但不限於K-means集群分析(K-Means Clustering)、蟻群演算法(Ant Colony Optimization, ACO)、粒子群優化演算法(Particle Swarm Optimization, PSO)。如圖13B所示,圖13B為示例性說明執行根據本發明第三實施例之區間影像23扣除不同的背景區間影像後進行放大運算,由於透過選定步驟S22''選擇較佳的區間影像23中的其中之一作為背景區間影像,使得放大後之區間影像23的影像訊號之波峰及波谷清晰且在訊號處理範圍內(如圖13B中所示之(23-8減23-6)*4以及(23-8減23-7)*4),有效消除區間影像23中的雜訊,造成影像強度資訊產生明顯的下降,使得根據本發明之動態影像21放大運算後的RV值(ridge valley value)進一步提升。Specifically, please refer to FIG. 13A and FIG. 13B , together with FIG. 12 . According to the third embodiment of the present invention, the actual algorithm execution process of the
藉此,本發明具有以下之實施功效及技術功效:Thus, the present invention has the following implementation effects and technical effects:
其一,藉由本發明之辨識系統100為基礎,並搭配本發明所提供之辨識方法,使得影像感測器12產生動態影像21以及透視影像22,透過動態影像21的變化過程以及透視影像22以判斷動態影像21是否為生物影像,如此一來,有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統,並且大幅增加辨識系統的安全性及辨識能力。Firstly, based on the
其二,藉由本發明之辨識系統100為基礎,並搭配本發明所提供之辨識方法,藉由執行演算法,該演算法將不同曝光區間32的區間影像23相互執行相減運算後取平均值,使得根據本發明之動態影像的RV值(ridge valley value)大幅提升,產生較佳的指紋辨識效果。藉此,大幅增進根據本發明之辨識系統100的接受誤差率(FAR)以及拒絕誤差率(FRR),達成高度準確性以及廣泛適用性等目的。Second, based on the
其三,根據本發明之辨識模組13有別於習知技術使用血管靜脈辨識作為辨識辦法,辨識模組13根據動態影像21的變化過程以及透視影像22根據透視影像22判斷動態影像21是否為生物影像,雙重認證有效防止他人以指紋之影像、圖片、或任意模型破解辨識系統100,同時辨識結果不需經過繁複的機器學習機制以及累積大量辨識特徵,使得離線辨識的可行性大幅提高,兼具廣泛適用性及高度安全性。Third, the
其四,在本發明第三實施例中,透過選定步驟S23''選擇較佳的區間影像23中的其中之一者作為背景區間影像,有效消除區間影像23中的雜訊,造成影像強度資訊產生明顯的下降,使得根據本發明之動態影像21放大運算後的RV值(ridge valley value)進一步提升。Fourth, in the third embodiment of the present invention, one of the better interval images 23 is selected as the background interval image through the selection step S23'' to effectively eliminate the noise in the interval image 23, resulting in image intensity information An obvious drop occurs, which further increases the RV value (ridge valley value) of the
以上係藉由特定的具體實施例說明本發明之實施方式,所屬技術領域具有通常知識者可由本說明書所揭示之內容輕易地瞭解本發明之其他優點及功效。The above is to illustrate the implementation of the present invention through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.
以上所述僅為本發明之較佳實施例,並非用以限定本發明之範圍;凡其它未脫離本發明所揭示之精神下所完成之等效改變或修飾,均應包含在下述之專利範圍內。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention; all other equivalent changes or modifications that do not deviate from the spirit disclosed in the present invention should be included in the scope of the following patents Inside.
100:辨識系統 11:感測區域 12:影像感測器 121:光感測器 122:互補性氧化金屬半導體 13:辨識模組 14:處理模組 15:運算模組 21:動態影像 22:透視影像 23、23-1、23-2、23-3、23-4、23-5、23-6:區間影像 31:時間範圍 32:曝光區間 200:待測物 S11:啟動步驟 S12:感測步驟 S13:辨識步驟 S21:啟動步驟 S22:相減步驟 S23:辨識步驟 S11':啟動步驟 S12':感測步驟 S13':運算步驟 S14':辨識步驟 S21':啟動步驟 S22':相減步驟 S23':訊號放大步驟 S24':平均步驟 S25':辨識步驟 S21'':啟動步驟 S22'':選定步驟 S23'':相減步驟 S24'':訊號放大步驟 S25'':平均步驟 S26'':辨識步驟 100: Identification system 11: Sensing area 12: Image sensor 121: Light sensor 122: Complementary metal oxide semiconductors 13: Identification module 14: Processing module 15: Operation module 21: Dynamic image 22: Perspective image 23, 23-1, 23-2, 23-3, 23-4, 23-5, 23-6: interval images 31: time frame 32: Exposure interval 200: The object to be tested S11: Startup steps S12: Sensing step S13: identification step S21: start step S22: subtraction step S23: identification step S11': start step S12': sensing step S13': operation steps S14': identification step S21': start step S22': subtraction step S23': Signal amplification step S24': average step S25': identification step S21'': start step S22'': selected step S23'': subtraction step S24'': Signal amplification step S25'': average step S26'': identification step
圖1為根據本發明之辨識系統的示意圖; 圖2為說明執行本發明之辨識方法的步驟方塊圖; 圖3為說明根據本發明之辨識方法實際執行過程之步驟流程圖; 圖4為根據本發明第一實施例之辨識系統的示意圖; 圖5為根據本發明第一實施例之影像感測器的示意圖; 圖6為說明根據本發明第一實施例之辨識方法的步驟方塊圖; 圖7為說明執行本發明第一實施例之辨識方法的又一步驟方塊圖; 圖8為說明根據本發明第一實施例之辨識方法實際執行過程之步驟流程圖; 圖9為根據本發明第二實施例之辨識系統的示意圖; 圖10為說明根據本發明第二實施例之辨識方法的步驟方塊圖; 圖11A為示例性說明根據本發明第二實施例之區間影像的示意圖; 圖11B為示例性說明根據本發明第二實施例之區間影像執行演算法後的示意圖; 圖12為說明根據本發明第三實施例之辨識方法的步驟方塊圖; 圖13A為示例性說明根據本發明第三實施例之區間影像執行演算法後的示意圖; 圖13B為示例性說明根據本發明第三實施例之區間影像執行演算法後的又一示意圖。 1 is a schematic diagram of an identification system according to the present invention; Fig. 2 is a block diagram illustrating the steps of implementing the identification method of the present invention; Fig. 3 is a flow chart illustrating the steps of the actual execution process of the identification method according to the present invention; 4 is a schematic diagram of an identification system according to a first embodiment of the present invention; 5 is a schematic diagram of an image sensor according to a first embodiment of the present invention; 6 is a block diagram illustrating the steps of the identification method according to the first embodiment of the present invention; FIG. 7 is a block diagram illustrating another step of implementing the identification method of the first embodiment of the present invention; FIG. 8 is a flow chart illustrating the steps of the actual execution process of the identification method according to the first embodiment of the present invention; 9 is a schematic diagram of an identification system according to a second embodiment of the present invention; 10 is a block diagram illustrating steps of an identification method according to a second embodiment of the present invention; FIG. 11A is a schematic diagram illustrating an interval image according to a second embodiment of the present invention; FIG. 11B is a schematic diagram illustrating an algorithm performed on an interval image according to the second embodiment of the present invention; 12 is a block diagram illustrating steps of an identification method according to a third embodiment of the present invention; FIG. 13A is a schematic diagram illustrating an algorithm performed on an interval image according to a third embodiment of the present invention; FIG. 13B is another schematic diagram illustrating an algorithm after the interval image is executed according to the third embodiment of the present invention.
100:辨識系統 100: Identification system
11:感測區域 11: Sensing area
12:影像感測器 12: Image sensor
13:辨識模組 13: Identification module
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