TW202127280A - Optical in vivo feature detection method and biometric acquisition device and information processing device using the same wherein the under-display optical biometric acquisition device comprises a photodetector circuit and a biometric acquisition circuit - Google Patents

Optical in vivo feature detection method and biometric acquisition device and information processing device using the same wherein the under-display optical biometric acquisition device comprises a photodetector circuit and a biometric acquisition circuit Download PDF

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TW202127280A
TW202127280A TW109101249A TW109101249A TW202127280A TW 202127280 A TW202127280 A TW 202127280A TW 109101249 A TW109101249 A TW 109101249A TW 109101249 A TW109101249 A TW 109101249A TW 202127280 A TW202127280 A TW 202127280A
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detection method
living body
biometric acquisition
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TWI730589B (en
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馮繼雄
陳子軒
王長海
田志民
李保梁
劉小寧
陳世林
宋子明
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大陸商北京集創北方科技股份有限公司
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Abstract

The present invention mainly discloses an in vivo feature detection method using an under-display optical biometric acquisition device. The under-display optical biometric acquisition device comprises a photodetector circuit and a biometric acquisition circuit. In the case of applying the in vivo feature detection method of the present invention, the under-display optical biometric acquisition device can not only achieve user identity authentication through biometric acquisition and matching, but also determine whether the tested biometrics comes from a living body through the method of detecting living features, such that the under-display optical biometric acquisition device will not be deceived by copies containing biological features.

Description

光學式活體特徵檢測方法及利用其之生物特徵採集裝置和資訊處理裝置Optical living body feature detection method and biological feature acquisition device and information processing device using the same

本發明係關於生物活體特徵之技術領域，尤指一種運用屏下光學式生物特徵採集裝置之活體特徵檢測方法。The present invention relates to the technical field of biological characteristics of living organisms, and particularly refers to a method for detecting biological characteristics using an under-screen optical biological characteristic acquisition device.

生物辨識技術(Biometric identification)係藉由採集人體固有的生理特徵作為個體生物的辨識依據，例如：虹膜(Iris)、臉部(Face)、聲紋(Voice)、與指紋(Fingerprint)等生理特徵。目前，隨著全屏幕智能手機逐漸成為主流，屏下式光學式生物特徵(指紋)辨識裝置已經廣泛地整合在全屏幕智能手機之中。Biometric identification technology uses the inherent physiological characteristics of the human body as the basis for identification of individual organisms, such as: iris (Iris), face (Face), voice print (Voice), fingerprint (Fingerprint) and other physiological characteristics . At present, as full-screen smart phones have gradually become the mainstream, under-screen optical biometric (fingerprint) identification devices have been widely integrated into full-screen smart phones.

圖1顯示習知的一種屏下式光學式生物特徵識別裝置的方塊圖，圖2顯示採用點光源採集方法之習知的屏下式光學式生物特徵採集裝置的架構圖。如圖1與圖2所示，習知的屏下式光學式生物特徵採集裝置的構成主要包含一光檢測器電路2’和一生物特徵採集與識別電路1’，其中該光檢測器陣列2’整合在智能手機的觸控顯示屏幕3’的下方處。在觸控顯示屏幕3’的一顯示面板31’的多個點光源32’照射一生物單元4’(例如手指或手掌)的情況下，該光檢測器陣列2’可取得對應於該生物單元4’之一生物特徵光信號。進一步地，後端的生物特徵採集與識別1’將該生物特徵光信號還原成多個採集圖像，從而進一步地將所述多個採集圖像處理成一生物特徵圖像，接著將該生物特徵圖像與預錄的一生物特徵圖像進行匹配，完成用戶身分識別。FIG. 1 shows a block diagram of a conventional under-screen optical biometric identification device, and FIG. 2 shows a structure diagram of a conventional under-screen optical biometric collection device using a point light source collection method. As shown in Figures 1 and 2, the conventional under-screen optical biometrics collection device mainly includes a photodetector circuit 2'and a biometrics collection and identification circuit 1', wherein the photodetector array 2 'Integrated under the touch display screen 3'of the smartphone. In the case that a plurality of point light sources 32' of a display panel 31' of the touch display screen 3'illuminate a biological unit 4'(for example, a finger or a palm), the photodetector array 2'can be obtained corresponding to the biological unit 4' 4'One of the biometric light signals. Further, the back-end biometric collection and recognition 1'restores the biometric light signal into a plurality of collected images, thereby further processing the plurality of collected images into a biometric image, and then the biometric image The image is matched with a pre-recorded biometric image to complete user identity recognition.

實務經驗指出，在應用生物特徵識別實現用戶身份認證的過程中，B人員可以手持含有A人員之生物特徵的複印品成功欺騙運用生物特徵識別之身份辨識系統。舉例而言，B人員可以令印有B人員指紋的紙張、膠帶、照片、錄影片段、或3D列印品令身份辨識系統所具有之生物特徵採集裝置得以完成生物特徵採集與識別，藉此方式達到欺騙所述身份辨識系統之效果。Practical experience points out that in the process of applying biometrics to realize user identity authentication, person B can successfully deceive the identity recognition system using biometrics by holding a copy of person A's biometrics. For example, Person B can order paper, tape, photos, video clips, or 3D prints printed with Person B’s fingerprints to enable the biometric collection device of the identity recognition system to complete biometric collection and identification. Achieve the effect of deceiving the identity recognition system.

有鑑於此，一些智能手機利用前鏡頭配合遙測式體積變化描記圖法(Remote photoplethysmography, rPPG)或成像式體積變化描記圖法(Imaging photoplethysmography, iPPG)量測用戶的脈搏及/或心率等活體特徵，從而利用這些活體特徵達成活體檢測，避免智能手機所搭載的生物特徵採集與識別裝置受到假影像的欺騙。然而，rPPG或iPPG之技術需要很高運算量，因此只適用於含有具高速運算能力之處理晶片組的高階智能手機。In view of this, some smart phones use the front lens with remote photoplethysmography (remote photoplethysmography, rPPG) or imaging photoplethysmography (iPPG) to measure the user's pulse and/or heart rate and other vital characteristics , So as to use these living body characteristics to achieve living body detection, avoiding the fraud of the fake image by the biometric collection and recognition device carried by the smart phone. However, rPPG or iPPG technology requires a high amount of computing, so it is only suitable for high-end smartphones with processing chipsets with high-speed computing capabilities.

屏下超音波指紋檢測技術為具有高穿透性、高抗污漬能力且支持活體檢測之生物特徵採集與識別技術，其除了可以採集獲得3D指紋圖像之外，還可以進行脈搏檢測，達到活體識別的效果。可惜的是，必須要智能手機內部額外增設超音波收/發單元才能夠實現所述屏下超音波指紋檢測技術。The under-screen ultrasonic fingerprint detection technology is a biometric collection and recognition technology that has high penetration, high stain resistance and supports live detection. In addition to acquiring 3D fingerprint images, it can also perform pulse detection to reach a living body. The effect of recognition. It is a pity that it is necessary to add an additional ultrasonic receiving/transmitting unit inside the smart phone to realize the under-screen ultrasonic fingerprint detection technology.

由上述說明可知，本領域亟需一種運用屏下光學式生物特徵採集裝置之活體特徵檢測方法。It can be seen from the above description that there is an urgent need in the art for a method for detecting biological features using an under-screen optical biological feature acquisition device.

本發明之一目的在於提供一種活體特徵檢測方法，其可藉由控制一屏下光學式生物特徵採集裝置的光源照射方式進行一生物特徵採集操作，以便於辨識一使用者的身分。An object of the present invention is to provide a method for detecting biological characteristics, which can perform a biological characteristic collecting operation by controlling the light source illumination mode of an under-screen optical biological characteristic collecting device to facilitate the identification of a user.

本發明之另一目的在於提供一種活體特徵檢測方法，其可在不增加硬體成本的情況下，有效獲取並辨識一使用者的生物特徵。Another object of the present invention is to provide a biological feature detection method, which can effectively acquire and recognize a user's biological feature without increasing the hardware cost.

本發明之又一目的在於提供一種活體特徵檢測方法，其可判斷受檢的生物特徵是否來自於一活體，以避免資訊系統受到含有生物特徵之複印品的欺騙。Another object of the present invention is to provide a living body feature detection method, which can determine whether the examined biological feature comes from a living body, so as to prevent the information system from being deceived by the copy containing the biological feature.

為達成上述目的，本發明提出所述活體特徵檢測方法的一實施例，其係利用一屏下光學式生物特徵採集裝置實現，該屏下光學式生物特徵採集裝置具有一光檢測器電路以及一生物特徵採集電路，且該光檢測器電路整合在一顯示屏幕下方處；所述活體特徵檢測方法包括以下步驟：In order to achieve the above objective, the present invention proposes an embodiment of the living body feature detection method, which is implemented by an under-screen optical biological feature collection device, which has a photodetector circuit and a photodetector circuit. A biological feature collection circuit, and the photodetector circuit is integrated at the bottom of a display screen; the living body feature detection method includes the following steps:

(1)在一生物單元碰觸該顯示屏幕的情況下，令該生物單元所碰觸的該顯示屏幕之一屏幕區域不發光，從而採集該生物單元之一採集表面的輪廓；(1) When a biological unit touches the display screen, make a screen area of the display screen touched by the biological unit do not emit light, so as to collect the contour of a collection surface of the biological unit;

(2)自所述屏幕區域內選擇N個點光源照射該採集表面， N至少為1；(2) Select N point light sources from the screen area to illuminate the collection surface, and N is at least 1;

(3)在該生物單元保持觸碰該顯示屏幕的情況下，連續採集M張生物特徵圖像，其中M為正整數，且各所述生物特徵圖像含有N個亮點；(3) When the biological unit keeps touching the display screen, continuously collect M biological characteristic images, where M is a positive integer, and each of the biological characteristic images contains N bright spots;

(4)獲取各所述生物特徵圖像所含有之所述N個亮點的一平均亮點半徑，共取得M個所述平均亮點半徑；以及(4) Obtain an average bright spot radius of the N bright spots contained in each of the biological feature images, and obtain a total of M of the average bright spot radii; and

(5)對M個所述平均亮點半徑執行一信號處理，從而獲得至少一活體特徵。(5) Perform a signal processing on the M average bright spot radii, so as to obtain at least one feature of a living body.

在可能的實施例中，該生物單元可為手指、手掌、或臉部。In possible embodiments, the biological unit may be a finger, a palm, or a face.

在可能的實施例中，該點光源可為有機發光二極體(Organic light-emitting diode, OLED)、次毫米發光二極體(Mini LED)或微發光二極體(Micro LED)。In possible embodiments, the point light source may be an organic light-emitting diode (OLED), a sub-millimeter light-emitting diode (Mini LED), or a micro-light-emitting diode (Micro LED).

在可能的實施例中，所述信號處理可為快速傅立葉轉換(Fast Fourier Transform, FFT)、離散快速傅立葉轉換(Discrete Fourier Transform, DFT)或短時距傅立葉變換(Short-Time Fourier Transform, STFT)。In a possible embodiment, the signal processing may be Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT) or Short-Time Fourier Transform (STFT) .

在可能的實施例中，所述信號處理可為奇異譜分析(Singular Spectrum Analysis, SSA)或正規化最小均方(Normalized Least Mean Square, NLMS)。In a possible embodiment, the signal processing may be Singular Spectrum Analysis (SSA) or Normalized Least Mean Square (NLMS).

在可能的實施例中，各所述光源可以一單波長光或一多波長光照射該生物單元的該採集表面。In a possible embodiment, each of the light sources can illuminate the collection surface of the biological unit with a single-wavelength light or a multi-wavelength light.

在可能的實施例中，該活體特徵可為心率(Heart rate, HR)、呼吸率(Respiratory rate, RR)或脈搏(Pulse)。In a possible embodiment, the vital feature may be Heart rate (HR), Respiratory rate (RR) or Pulse (Pulse).

為達成上述目的，本發明進一步提出一種生物特徵採集裝置，其係由如前述之屏下光學式生物特徵採集裝置實現。In order to achieve the above objective, the present invention further provides a biological feature collection device, which is implemented by the aforementioned under-screen optical biological feature collection device.

另外，本發明亦提出一種資訊處理裝置，其具有一中央處理單元及如前述之屏下式光學式生物特徵採集裝置，其中，該中央處理單元係用以接收該屏下光學式生物特徵採集裝置所提供的所述至少一活體特徵的資料。In addition, the present invention also provides an information processing device, which has a central processing unit and the aforementioned under-screen optical biometrics collection device, wherein the central processing unit is used to receive the under-screen optical biometrics collection device The provided data of the at least one biological feature.

在可能的實施例中，所述資訊處理裝置可為智能手機、平板電腦、筆記型電腦、一體式電腦、智能手錶或門禁裝置。In possible embodiments, the information processing device may be a smart phone, a tablet computer, a notebook computer, an all-in-one computer, a smart watch, or an access control device.

為使  貴審查委員能進一步瞭解本發明之結構、特徵、目的、與其優點，茲附以圖式及較佳具體實施例之詳細說明如後。In order to enable your reviewer to further understand the structure, features, purpose, and advantages of the present invention, the drawings and detailed descriptions of preferred specific embodiments are attached as follows.

圖3顯示應用本發明之一種活體特徵檢測方法的一屏下式光學式生物特徵採集裝置的方塊圖，且圖4顯示應用本發明之活體特徵檢測方法的屏下式光學式生物特徵採集裝置的採集架構圖。本發明之活體特徵檢測方法係應用於一屏下光學式生物特徵採集裝置之中，從而運用屏下光學式生物特徵採集裝置完成一活體特徵檢測，使該屏下光學式生物特徵採集裝置因具有活體識別功能而不會受到含有生物特徵的複印品之欺騙。如圖3與圖4所示，該屏下光學式生物特徵採集裝置具有一光檢測器電路2以及一生物特徵採集電路1，且該光檢測器電路2整合在智能手機的一顯示屏幕3的一顯示面板31的下方處。FIG. 3 shows a block diagram of an under-screen optical biometrics acquisition device applying a living body feature detection method of the present invention, and FIG. 4 shows an under-screen optical biometrics acquisition device applying the living feature detection method of the present invention Acquisition architecture diagram. The living body feature detection method of the present invention is applied to an under-screen optical biological feature collection device, so that the under-screen optical biological feature collection device is used to complete a living body feature detection, so that the under-screen optical biological feature collection device has Living body recognition function will not be deceived by the photocopies containing biological characteristics. As shown in Figures 3 and 4, the under-screen optical biometric collection device has a photodetector circuit 2 and a biometric collection circuit 1, and the photodetector circuit 2 is integrated in a display screen 3 of a smart phone. Below a display panel 31.

圖5顯示本發明之一種生物特徵採集與識別方法的流程圖。如圖3、圖4與圖5所示，本發明之生物特徵採集與識別方法的流程係首先執行步驟S1：在一生物單元4碰觸該顯示屏幕3的情況下，令該生物單元4所碰觸的該顯示屏幕3之一屏幕區域不發光，從而採集該生物單元4之一採集表面的輪廓。接著，執行步驟S2與步驟S3，進以自所述屏幕區域內選擇N個點光源32照射該採集表面，從而在該生物單元4保持觸碰該顯示屏幕3的情況下，連續採集M張生物特徵圖像｛A1 ,A2 ,…,AM ｝，其中M為正整數且N至少為1。值得說明的是，各所述生物特徵圖像含有N個亮點｛DM1 ,DM2 ,…,DMN ｝。Fig. 5 shows a flow chart of a method for collecting and identifying biological characteristics of the present invention. As shown in Figures 3, 4, and 5, the flow of the biological feature collection and recognition method of the present invention first executes step S1: when a biological unit 4 touches the display screen 3, the biological unit 4 is The touched screen area of the display screen 3 does not emit light, so that the contour of the collection surface of the biological unit 4 is collected. Then, step S2 and step S3 are performed to select N point light sources 32 from the screen area to illuminate the collection surface, so that while the biological unit 4 keeps touching the display screen 3, continuously collect M biological images. Feature image {A 1 ,A 2 ,...,A M }, where M is a positive integer and N is at least 1. It is worth noting that each of the biometric images contains N bright spots {D M1 , D M2 ,..., D MN }.

在可行的實施例中，所述該生物單元4為選自於由手指、手掌、與臉部所組成之群組的一種具生物特徵之生物部位。並且，在一實施例中，所述點光源可以是有機發光二極體(Organic light-emitting diode, OLED)、次毫米發光二極體(Mini LED)、或微發光二極體(Micro LED)，其用以發出一單波長光或一多波長光照射該生物單元4的該採集表面。In a feasible embodiment, the biological unit 4 is a biological part with biological characteristics selected from the group consisting of fingers, palms, and faces. Moreover, in an embodiment, the point light source may be an organic light-emitting diode (OLED), a sub-millimeter light-emitting diode (Mini LED), or a micro-light-emitting diode (Micro LED). , Which is used to emit a single-wavelength light or a multi-wavelength light to illuminate the collection surface of the biological unit 4.

圖6顯示利用本發明之活體特徵檢測方法所取得的M個生物特徵(指紋)圖像的影像圖。如圖6所示，所採集到的每張(第M張)生物特徵圖像6含有3個亮點61｛DM1 ,DM2 ,…,DM3 ｝，且圖6中的虛線圓係表示各所述亮點61皆具有一個亮點半徑｛rM1 ,rM2 ,…,rM3 ｝。繼續地，方法流程係執行步驟S4：獲取各所述生物特徵圖像6所含有之所述N個亮點61的一平均亮點半徑，共取得M個所述平均亮點半徑｛R1 ,R2 ,…,RM ｝。圖7即顯示利用本發明之活體特徵檢測方法所取得之M個平均亮點半徑的資料曲線圖。FIG. 6 shows an image diagram of M biometric (fingerprint) images obtained by using the living body feature detection method of the present invention. As shown in Figure 6, each (M-th) biometric image 6 collected contains three bright spots 61 {D M1 , D M2 ,..., D M3 }, and the dotted circles in Figure 6 indicate each The bright spots 61 all have a bright spot radius {r M1 , r M2 ,..., r M3 }. Continuing, the method flow is to perform step S4: obtain an average bright spot radius of the N bright spots 61 contained in each of the biometric images 6, and obtain a total of M average bright spot radii {R 1 , R 2 , …, R M }. FIG. 7 is a graph showing the data curve of M average bright spot radii obtained by the living body feature detection method of the present invention.

在獲得M個所述平均亮點半徑｛R1 ,R2 ,…,RM ｝的資料之後，方法流程最終執行步驟S5：對M個所述平均光圈半徑｛R1 ,R2 ,…,RM ｝執行一信號處理，從而獲得至少一活體特徵。在可行的實施例中，所述活體特徵可以是選自於由心率(Heart rate, HR)、呼吸率(Respiratory rate, RR)與脈搏(Pulse)所組成之群組的一種生理特徵。因此，本發明也不限定所述信號處理的類型，其可以是一頻域信號處理或一時域信號處理。以頻域信號處理而言，可以選用快速傅立葉轉換(Fast Fourier Transform, FFT)、離散快速傅立葉轉換(Discrete Fourier Transform, DFT)或短時距傅立葉變換(Short-Time Fourier Transform, STFT)。以時域信號處理而言，則可以選用奇異譜分析(Singular Spectrum Analysis, SSA)或正規化最小均方(Normalized Least Mean Square, NLMS)。After obtaining M data of the average bright spot radius {R 1 , R 2 ,..., R M }, the method flow finally executes step S5: for the M average aperture radii {R 1 ,R 2 ,...,R M ｝ performs a signal processing to obtain at least one feature of the living body. In a feasible embodiment, the biological characteristic may be a physiological characteristic selected from the group consisting of heart rate (HR), respiratory rate (RR), and pulse (Pulse). Therefore, the present invention does not limit the type of signal processing, which may be a frequency domain signal processing or a time domain signal processing. In terms of frequency domain signal processing, Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), or Short-Time Fourier Transform (STFT) can be selected. In terms of time-domain signal processing, Singular Spectrum Analysis (SSA) or Normalized Least Mean Square (NLMS) can be selected.

利用離散快速傅立葉轉換(DFT)完成對於M個所述平均光圈半徑｛R1 ,R2 ,…,RM ｝的該信號處理之後，圖8即顯示利用本發明之活體特徵檢測方法所取得之離散快速傅立葉轉換的輸出資料曲線圖。如圖8所示，菱形資料點表示在0.7至4Hz的範圍之振幅最大值，而將對應於該菱形資料點(振幅最大值)的頻率值乘上60之後(亦即f*60)，即可獲得脈搏數。例如，圖8的菱形資料點所對應的頻率值約為1.125，如此則可進一步計算出脈搏數為1.125*60=67.5。應知道，正常人的脈搏數值為60-100 次/分。After using Discrete Fast Fourier Transform (DFT) to complete the signal processing for the M average aperture radii {R 1 , R 2 ,..., R M }, FIG. 8 shows the results obtained by using the living body feature detection method of the present invention The output data curve graph of Discrete Fast Fourier Transform. As shown in Figure 8, the diamond-shaped data point represents the maximum amplitude in the range of 0.7 to 4 Hz, and the frequency value corresponding to the diamond-shaped data point (the maximum amplitude) is multiplied by 60 (that is, f*60), that is Get the pulse rate. For example, the frequency value corresponding to the diamond-shaped data point in Fig. 8 is about 1.125, so the pulse rate can be further calculated as 1.125*60=67.5. It should be known that the pulse value of a normal person is 60-100 beats per minute.

依上述的說明，本發明可進一步提供一種資訊處理裝置，其具有一中央處理單元及如前述之屏下式光學式生物特徵採集裝置，其中，該中央處理單元係用以接收該屏下光學式生物特徵採集裝置所提供的所述至少一活體特徵的資料。另外，在可能的實施例中，所述資訊處理裝置可為智能手機、平板電腦、筆記型電腦、一體式電腦、智能手錶或門禁裝置。According to the above description, the present invention can further provide an information processing device, which has a central processing unit and the aforementioned under-screen optical biometrics acquisition device, wherein the central processing unit is used to receive the under-screen optical biometrics The data of the at least one biological feature provided by the biological feature collection device. In addition, in possible embodiments, the information processing device may be a smart phone, a tablet computer, a notebook computer, an all-in-one computer, a smart watch, or an access control device.

如此，上述已完整且清楚地說明本發明之一種運用屏下光學式生物特徵採集裝置之活體特徵檢測方法；並且，經由上述可得知本發明具有下列優點：In this way, the above has completely and clearly explained a living body feature detection method of the present invention using an under-screen optical biometric feature collection device; and, from the above, it can be seen that the present invention has the following advantages:

(1)本發明的活體特徵檢測方法可藉由控制一屏下光學式生物特徵採集裝置的光源照射方式進行一生物特徵採集操作，以便於辨識一使用者的身分。(1) The biological feature detection method of the present invention can perform a biological feature collection operation by controlling the light source illumination mode of an under-screen optical biological feature collection device to facilitate the identification of a user.

(2)本發明的活體特徵檢測方法可在不增加硬體成本的情況下，有效獲取並辨識一使用者的生物特徵。(2) The biological feature detection method of the present invention can effectively acquire and recognize a user's biological feature without increasing the hardware cost.

(3)本發明的活體特徵檢測方法可判斷受檢的生物特徵是否來自於一活體，以避免資訊系統受到含有生物特徵之複印品的欺騙。(3) The living body feature detection method of the present invention can determine whether the examined biological feature comes from a living body, so as to prevent the information system from being deceived by the copy containing the biological feature.

必須加以強調的是，前述本案所揭示者乃為較佳實施例，舉凡局部之變更或修飾而源於本案之技術思想而為熟習該項技藝之人所易於推知者，俱不脫本案之專利權範疇。It must be emphasized that the foregoing disclosures in this case are preferred embodiments, and any partial changes or modifications that are derived from the technical ideas of this case and are easily inferred by those who are familiar with the art will not deviate from the patent of this case. Right category.

綜上所陳，本案無論目的、手段與功效，皆顯示其迥異於習知技術，且其首先發明合於實用，確實符合發明之專利要件，懇請  貴審查委員明察，並早日賜予專利俾嘉惠社會，是為至禱。In summary, regardless of the purpose, means and effect of this case, it is shown that it is very different from the conventional technology, and its first invention is suitable for practicality, and it does meet the patent requirements of the invention. I implore the examiner to check it out and grant the patent as soon as possible. Society is for the best prayer.

＜本發明＞ 1:生物特徵採集電路 2:光檢測器電路 3:顯示屏幕 31:顯示面板 32:點光源 4:生物單元 6:生物特徵圖像 61:亮點 步驟S1:在一生物單元碰觸該顯示屏幕的情況下，令該生物單元所碰觸的該顯示屏幕之一屏幕區域不發光，從而採集該生物單元之一採集表面的輪廓 步驟S2:自所述屏幕區域內選擇N個點光源照射該採集表面 步驟S3:在該生物單元保持觸碰該顯示屏幕的情況下，連續採集M張生物特徵圖像，其中M為正整數，且各所述生物特徵圖像含有N個亮點 步驟S4:獲取各所述生物特徵圖像所含有之所述N個亮點的一平均亮點半徑，共取得M個所述平均亮點半徑 步驟S5:對M個所述平均光圈半徑執行一信號處理，從而獲得至少一活體特徵<The present invention> 1: Biometrics acquisition circuit 2: Light detector circuit 3: display screen 31: display panel 32: Point light source 4: Biological unit 6: Biometric image 61: Highlights Step S1: When a biological unit touches the display screen, make a screen area of the display screen touched by the biological unit not emit light, so as to collect the contour of a collection surface of the biological unit Step S2: Select N point light sources from the screen area to illuminate the collection surface Step S3: While the biological unit keeps touching the display screen, continuously collect M biometric images, where M is a positive integer, and each of the biometric images contains N bright spots Step S4: Obtain an average bright spot radius of the N bright spots contained in each of the biological feature images, and obtain a total of M average bright spot radii Step S5: Perform a signal processing on the M average aperture radii, so as to obtain at least one living body feature

＜習知＞ 1’:生物特徵採集電路 2’:光檢測器電路 3’:觸控顯示屏幕 31’:顯示面板 32’:點光源 4’:生物單元＜Acquaintances＞ 1’: Biometrics acquisition circuit 2’: Light detector circuit 3’: Touch display screen 31’: Display panel 32’: Point light 4’: Biological unit

圖1為習知的一種屏下式光學式生物特徵識別裝置的方塊圖； 圖2為採用點光源採集方法之習知的屏下式光學式生物特徵採集裝置的架構圖； 圖3為應用本發明之一種活體特徵檢測方法的一屏下式光學式生物特徵採集裝置的方塊圖； 圖4為應用本發明之活體特徵檢測方法的屏下式光學式生物特徵採集裝置的採集架構圖； 圖5為本發明之一種生物特徵採集與識別方法的流程圖； 圖6為利用本發明之活體特徵檢測方法所取得的M個生物特徵(指紋)圖像的影像圖； 圖7為利用本發明之活體特徵檢測方法所取得之M個平均亮點半徑的資料曲線圖；以及 圖8為利用本發明之活體特徵檢測方法所取得之離散快速傅立葉轉換的輸出資料曲線圖。Figure 1 is a block diagram of a conventional under-screen optical biometric identification device; Figure 2 is a structural diagram of a conventional under-screen optical biometrics collection device using a point light source collection method; FIG. 3 is a block diagram of an under-screen optical biometric feature collection device using a living body feature detection method of the present invention; FIG. 4 is a diagram of the acquisition architecture of an under-screen optical biological characteristic acquisition device applying the living body characteristic detection method of the present invention; Fig. 5 is a flow chart of a method for collecting and identifying biological characteristics of the present invention; FIG. 6 is an image diagram of M biometric (fingerprint) images obtained by using the living body feature detection method of the present invention; FIG. 7 is a data curve diagram of M average bright spot radii obtained by using the living body feature detection method of the present invention; and FIG. 8 is a graph showing the output data of the discrete fast Fourier transform obtained by the method for detecting biological features of the present invention.

步驟S1:在一生物單元碰觸該顯示屏幕的情況下，令該生物單元所碰觸的該顯示屏幕之一屏幕區域不發光，從而採集該生物單元之一採集表面的輪廓Step S1: When a biological unit touches the display screen, make a screen area of the display screen touched by the biological unit not emit light, so as to collect the contour of a collection surface of the biological unit

步驟S2:自所述屏幕區域內選擇N個點光源照射該採集表面Step S2: Select N point light sources from the screen area to illuminate the collection surface

步驟S3:在該生物單元保持觸碰該顯示屏幕的情況下，連續採集M張生物特徵圖像，其中M為正整數，且各所述生物特徵圖像含有N個亮點Step S3: While the biological unit keeps touching the display screen, continuously collect M biometric images, where M is a positive integer, and each of the biometric images contains N bright spots

步驟S4:獲取各所述生物特徵圖像所含有之所述N個亮點的一平均亮點半徑，共取得M個所述平均亮點半徑Step S4: Obtain an average bright spot radius of the N bright spots contained in each of the biological feature images, and obtain a total of M average bright spot radii

步驟S5:對M個所述平均光圈半徑執行一信號處理，從而獲得至少一活體特徵Step S5: Perform a signal processing on the M average aperture radii, so as to obtain at least one living body feature

Claims (10)

一種活體特徵檢測方法，其係利用一屏下光學式生物特徵採集裝置實現，該屏下光學式生物特徵採集裝置具有一光檢測器電路以及一生物特徵採集電路，且該光檢測器電路整合在一顯示屏幕下方處；所述活體特徵檢測方法包括以下步驟： 在一生物單元碰觸該顯示屏幕的情況下，令該生物單元所碰觸的該顯示屏幕之一屏幕區域不發光，從而採集該生物單元之一採集表面的輪廓； 自所述屏幕區域內選擇N個點光源照射該採集表面， N至少為1； 在該生物單元保持觸碰該顯示屏幕的情況下，連續採集M張生物特徵圖像，其中M為正整數，且各所述生物特徵圖像含有N個亮點； 獲取各所述生物特徵圖像所含有之所述N個亮點的一平均亮點半徑，共取得M個所述平均亮點半徑；以及 對M個所述平均光圈半徑執行一信號處理，從而獲得至少一活體特徵。A living body feature detection method, which is realized by using an under-screen optical biological feature collection device, the under-screen optical biological feature collection device has a photodetector circuit and a biological feature collection circuit, and the photodetector circuit is integrated in 1. At the bottom of the display screen; the living body feature detection method includes the following steps: When a biological unit touches the display screen, make a screen area of the display screen touched by the biological unit do not emit light, so as to collect the contour of a collection surface of the biological unit; Select N point light sources from the screen area to illuminate the collection surface, and N is at least 1; When the biological unit keeps touching the display screen, continuously collect M biological characteristic images, where M is a positive integer, and each of the biological characteristic images contains N bright spots; Obtaining an average bright spot radius of the N bright spots contained in each of the biological feature images, and obtaining a total of M of the average bright spot radii; and A signal processing is performed on the M average aperture radii, so as to obtain at least one feature of a living body. 如申請專利範圍第1項所述之活體特徵檢測方法，其中，該生物單元為選自於由手指、手掌、與臉部所組成之群組的一種具生物特徵之生物部位。According to the method for detecting biological characteristics of the first item of the scope of patent application, the biological unit is a biological part with biological characteristics selected from the group consisting of fingers, palms, and faces. 如申請專利範圍第1項所述之活體特徵檢測方法，其中，該點光源為選自於由有機發光二極體、次毫米發光二極體、和微發光二極體所組成群組所選擇的一種發光元件。The biological feature detection method as described in item 1 of the scope of patent application, wherein the point light source is selected from the group consisting of organic light-emitting diodes, sub-millimeter light-emitting diodes, and micro-light-emitting diodes A kind of light-emitting element. 如申請專利範圍第1項所述之活體特徵檢測方法，其中，所述信號處理為選自於由快速傅立葉轉換(Fast Fourier Transform, FFT)、離散快速傅立葉轉換(Discrete Fourier Transform, DFT)與短時距傅立葉變換(Short-Time Fourier Transform, STFT)所組成之群組的一種頻域信號處理。According to the living body feature detection method described in claim 1, wherein the signal processing is selected from Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT) and Short A frequency-domain signal processing group formed by the Short-Time Fourier Transform (STFT). 如申請專利範圍第1項所述之活體特徵檢測方法，其中，所述信號處理為選自於由奇異譜分析(Singular Spectrum Analysis, SSA)與正規化最小均方(Normalized Least Mean Square, NLMS)所組成之群組的一種時域信號處理。The living body feature detection method described in item 1 of the scope of patent application, wherein the signal processing is selected from Singular Spectrum Analysis (SSA) and Normalized Least Mean Square (NLMS) A kind of time-domain signal processing of the group formed. 如申請專利範圍第1項所述之活體特徵檢測方法，其中，各所述光源係以一單波長光或一多波長光照射該生物單元的該採集表面。As described in the first item of the scope of patent application, the living body feature detection method, wherein each of the light sources irradiates the collection surface of the biological unit with a single-wavelength light or a multi-wavelength light. 如申請專利範圍第1項所述之活體特徵檢測方法，其中，該活體特徵為選自於由心率、呼吸率與脈搏所組成之群組的一種生理特徵。According to the method for detecting biological characteristics described in item 1 of the scope of patent application, the biological characteristics are a physiological characteristic selected from the group consisting of heart rate, respiration rate, and pulse. 一種生物特徵採集裝置，其係由如申請專利範圍第1至7項中任一項所述之屏下光學式生物特徵採集裝置實現。A biological feature collection device, which is realized by an under-screen optical biological feature collection device as described in any one of items 1 to 7 of the scope of patent application. 一種資訊處理裝置，其具有一中央處理單元及如前述之屏下式光學式生物特徵採集裝置，其中，該中央處理單元係用以接收該屏下光學式生物特徵採集裝置所提供的所述至少一活體特徵的資料。An information processing device, which has a central processing unit and the aforementioned under-screen optical biological feature collection device, wherein the central processing unit is used to receive the at least one provided by the under-screen optical biological feature collection device. A data on the characteristics of a living body. 如申請專利範圍第9項所述之資訊處理裝置，其係選自於由智慧型手機、平板電腦、筆記型電腦、一體式電腦、指紋式打卡裝置、和門禁裝置所組成之群組的一種電子裝置。The information processing device described in item 9 of the scope of patent application is selected from the group consisting of smart phones, tablet computers, notebook computers, all-in-one computers, fingerprint punching devices, and access control devices Electronic device.
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