TW202147170A - Under-screen fingerprint sensing device - Google Patents
Under-screen fingerprint sensing device Download PDFInfo
- Publication number
- TW202147170A TW202147170A TW109141947A TW109141947A TW202147170A TW 202147170 A TW202147170 A TW 202147170A TW 109141947 A TW109141947 A TW 109141947A TW 109141947 A TW109141947 A TW 109141947A TW 202147170 A TW202147170 A TW 202147170A
- Authority
- TW
- Taiwan
- Prior art keywords
- finger
- image sensor
- under
- image
- sensing device
- Prior art date
Links
- 238000000034 method Methods 0.000 claims abstract description 5
- 239000011521 glass Substances 0.000 claims description 20
- 230000002093 peripheral effect Effects 0.000 claims description 11
- 241001270131 Agaricus moelleri Species 0.000 claims 1
- 238000005286 illumination Methods 0.000 abstract description 22
- 230000010287 polarization Effects 0.000 description 22
- 238000010586 diagram Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 10
- 238000002310 reflectometry Methods 0.000 description 5
- 238000002834 transmittance Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000002708 enhancing effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 2
- 230000000149 penetrating effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000006059 cover glass Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
- G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
- G06V40/1388—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger using image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
- G06V40/1394—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger using acquisition arrangements
Landscapes
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Input (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Collating Specific Patterns (AREA)
Abstract
Description
本發明是有關於一種感測裝置,且特別是有關於一種指紋感測裝置。The present invention relates to a sensing device, and more particularly, to a fingerprint sensing device.
隨著可攜式電子裝置朝向大屏占比發展,原本置於電子裝置正面的電容式指紋感測器便不再適用,而需改成置於電子裝置的側面或背面。然而,置於側面或背面的指紋感測器在使用上有不便利之處,因此屏下式指紋感測器便被發展出來。With the development of portable electronic devices toward a larger screen-to-body ratio, the capacitive fingerprint sensor originally placed on the front of the electronic device is no longer applicable, and needs to be placed on the side or back of the electronic device. However, the fingerprint sensor placed on the side or the back is inconvenient to use, so the under-screen fingerprint sensor has been developed.
屏下式指紋感測器大致上可分為光學式指紋感測器與超聲波式指紋感測器,其中又以光學式指紋感測器成本較低且較適合量產。由於指紋感測攸關使用者的個人資料的安全性,因此,尚需發展出反欺騙(anti-spoofing)的功能。例如,欲竊取個人資料的人可能可以在環境中採集使用者的指紋,並製作成具有此指紋的假手指,再用假手指按壓電子裝置以達成指紋成功辨識而解鎖。因此,需發展出能夠辨識出假手指而非真手指的指紋感測器,以進一步提升使用者的個人資料的安全性。The under-screen fingerprint sensor can be roughly divided into an optical fingerprint sensor and an ultrasonic fingerprint sensor. Among them, the optical fingerprint sensor has a lower cost and is more suitable for mass production. Since fingerprint sensing is critical to the security of the user's personal data, an anti-spoofing function needs to be developed. For example, a person who wants to steal personal data may collect a user's fingerprint in the environment, make a fake finger with the fingerprint, and then use the fake finger to press the electronic device to achieve successful fingerprint recognition and unlock. Therefore, it is necessary to develop a fingerprint sensor capable of recognizing fake fingers instead of real fingers, so as to further enhance the security of user's personal data.
本發明提供一種屏下式指紋感測裝置,其可辨識出真手指與假手指。The present invention provides an under-screen fingerprint sensing device, which can identify a real finger and a fake finger.
本發明的一實施例提出一種屏下式指紋感測裝置,用以與一顯示器搭配,顯示器發出一照明光,照明光傳遞至按壓於顯示器上的一手指,手指將照明光反射成攜帶手指的指紋資訊的一訊號光。屏下式指紋感測裝置包括一影像感測器及一控制器。影像感測器配置於顯示器下方,其中訊號光穿透顯示器而在影像感測器上形成一指紋影像。控制器電性連接至影像感測器,處理影像感測器所感測到的指紋影像,對指紋影像的兩個方向不同的軸線上的灰階值與一哈爾特徵(Haar-like feature)作內積,並藉此判斷指紋影像是來自真手指或假手指。An embodiment of the present invention provides an under-screen fingerprint sensing device for matching with a display. The display emits an illuminating light, the illuminating light is transmitted to a finger pressed on the display, and the finger reflects the illuminating light into a fingerprint that carries the finger. A signal light for fingerprint information. The under-screen fingerprint sensing device includes an image sensor and a controller. The image sensor is disposed below the display, wherein the signal light penetrates the display to form a fingerprint image on the image sensor. The controller is electrically connected to the image sensor, and processes the fingerprint image sensed by the image sensor. The inner product is used to determine whether the fingerprint image is from a real finger or a fake finger.
本發明的一實施例提出一種屏下式指紋感測裝置,用以與一顯示器搭配,顯示器發出一照明光,照明光傳遞至按壓於顯示器上的一手指,手指將照明光反射成攜帶手指的指紋資訊的一訊號光。屏下式指紋感測裝置包括一影像感測器及一控制器。影像感測器配置於一顯示器下方,其中訊號光穿透顯示器而在影像感測器上形成一指紋影像。控制器電性連接至影像感測器,且用以處理影像感測器所感測到的指紋影像。控制器用以計算指紋影像的一中心區域的灰階平均值及在中心區域兩側的二周邊區域的各自的灰階平均值,並藉此判斷指紋影像是來自真手指或假手指。An embodiment of the present invention provides an under-screen fingerprint sensing device for matching with a display. The display emits an illuminating light, the illuminating light is transmitted to a finger pressed on the display, and the finger reflects the illuminating light into a fingerprint that carries the finger. A signal light for fingerprint information. The under-screen fingerprint sensing device includes an image sensor and a controller. The image sensor is disposed under a display, wherein the signal light penetrates the display to form a fingerprint image on the image sensor. The controller is electrically connected to the image sensor, and is used for processing the fingerprint image sensed by the image sensor. The controller is used for calculating the grayscale average value of a central area of the fingerprint image and the respective grayscale average values of the two peripheral areas on both sides of the central area, thereby determining whether the fingerprint image is from a real finger or a fake finger.
本發明的一實施例提出一種屏下式指紋感測裝置,用以與一顯示器搭配,顯示器發出一照明光,照明光傳遞至按壓於顯示器上的一手指,手指將照明光反射成攜帶手指的指紋資訊的一訊號光。屏下式指紋感測裝置包括一影像感測器及一控制器。影像感測器配置於一顯示器下方,其中訊號光穿透顯示器而在影像感測器上形成一指紋影像。控制器電性連接至影像感測器,且用以處理影像感測器所感測到的指紋影像。控制器用以對指紋影像的兩個方向不同的軸線上的灰階值與一哈爾特徵作內積,以獲得一第一結果。控制器用以計算指紋影像的一中心區域的灰階平均值及在中心區域兩側的二周邊區域的各自的灰階平均值,以獲得一第二結果。控制器用以綜合第一結果與第二結果來判斷指紋影像是來自真手指或假手指。An embodiment of the present invention provides an under-screen fingerprint sensing device for matching with a display. The display emits an illuminating light, the illuminating light is transmitted to a finger pressed on the display, and the finger reflects the illuminating light into a fingerprint that carries the finger. A signal light for fingerprint information. The under-screen fingerprint sensing device includes an image sensor and a controller. The image sensor is disposed under a display, wherein the signal light penetrates the display to form a fingerprint image on the image sensor. The controller is electrically connected to the image sensor, and is used for processing the fingerprint image sensed by the image sensor. The controller is used for performing an inner product on the grayscale values on two axes of the fingerprint image in different directions and a Haar feature to obtain a first result. The controller is used for calculating the grayscale average value of a central area of the fingerprint image and the respective grayscale average values of two peripheral areas on both sides of the central area to obtain a second result. The controller is used for combining the first result and the second result to determine whether the fingerprint image is from a real finger or a fake finger.
在本發明的實施例的屏下式指紋感測裝置中,控制器對指紋影像的兩個方向不同的軸線上的灰階值與一哈爾特徵作內積,及/或計算指紋影像的中心區域的灰階平均值及在中心區域兩側的二個周邊區域的各自的灰階平均值,並藉此判斷指紋影像是來自真手指或假手指。因此,本發明的實施例的屏下式指紋感測裝置在指紋感測上可以達到防欺騙的效果,進而提升使用者的個人資料的安全性。In the under-screen fingerprint sensing device of the embodiment of the present invention, the controller performs an inner product of the grayscale values on two axes of the fingerprint image in different directions and a Haar feature, and/or calculates the center of the fingerprint image The gray level average value of the area and the respective gray level average values of the two peripheral areas on both sides of the central area are used to determine whether the fingerprint image is from a real finger or a fake finger. Therefore, the under-screen fingerprint sensing device of the embodiments of the present invention can achieve the effect of anti-spoofing in fingerprint sensing, thereby enhancing the security of the user's personal data.
圖1為本發明的一實施例的電子裝置的剖面示意圖。請參照圖1,本實施例的電子裝置100包括一顯示器105及一屏下式指紋感測裝置200。電子裝置100例如為智慧型手機、平板電腦、筆記型電腦、個人數位助理(personal digital assistant, PDA)或其他適當的電子裝置。屏下式指紋感測裝置200用以與一顯示器105搭配,且包括一影像感測器210及一控制器230。影像感測器210配置於顯示器105下方。顯示器105發出一照明光60,照明光60傳遞至按壓於顯示器105上的一手指50,手指50將照明光60反射成攜帶手指的指紋資訊的一訊號光70。訊號光70穿透顯示器105而在影像感測器210上形成一指紋影像。如此一來,影像感測器210便能夠感測到手指50的指紋影像。控制器230電性連接至影像感測器210,且用以處理影像感測器210所感測到的指紋影像。FIG. 1 is a schematic cross-sectional view of an electronic device according to an embodiment of the present invention. Referring to FIG. 1 , the
在本實施例中,顯示器105包括一玻璃蓋(cover glass)120、一線偏振片116、一相位延遲膜114及一有機發光二極體顯示面板112。手指按壓於玻璃蓋120上。線偏振片116配置於玻璃蓋120與影像感測器210之間。相位延遲膜114配置於線偏振片116與影像感測器210之間,其中相位延遲膜114例如為波片(wave plate),其可以是顯示器本身的材料所形成,或是額外加入的波片所形成。有機發光二極體顯示面板112配置於相位延遲膜114與影像感測器210之間。在其他實施例中,亦可以用液晶顯示面板、微發光二極體顯示面板、電泳顯示面板或其他適當的顯示面板來取代有機發光二極體顯示面板112。In this embodiment, the
在本實施例中,屏下式指紋感測裝置200更包括一鏡頭220,配置於訊號光70的路徑上,且位於顯示器105與影像感測器210之間。鏡頭220可包括一或多片透鏡,其可將訊號光70成像於影像感測器210上,以在影像感測器210上形成指紋影像。In this embodiment, the under-screen
具體而言,有機發光二極體顯示面板112發出的照明光60是不具有偏振性的,且當其穿透相位延遲膜114後,仍不具有偏振性。然而,當照明光60穿透線偏振片116後,會具有偏振方向K0,其中偏振方向K0可被分解成第一偏振方向K1與第二偏振方向K2兩個分量,如圖2A及圖2B所示,第一偏振方向K1為P偏振方向,而第二偏振方向K2為S偏振方向。換言之,具有偏振方向K0的照明光60可視為具有第一偏振方向K1的照明光62與具有第二偏振方向K2的照明光64所合成。Specifically, the
圖2A繪示圖1中具有P偏振方向的照明光被手指反射的情形,而圖2B繪示圖1中具有S偏振方向的照明光被手指反射的情形。請參照圖1、圖2A及圖2B,具有第一偏振方向K1的照明光62在玻璃蓋120中行進(如圖2A),並在玻璃蓋120的上表面122處被手指50部分反射成訊號光72,而另一部分的照明光82則在手指50中傳遞。另一方面,具有第二偏振方向K2的照明光64在玻璃蓋120中行進(如圖2B),並在玻璃蓋120的上表面122處被手指50部分反射成訊號光74,而另一部分的照明光84則在手指50中傳遞。其中,訊號光72與訊號光74合成訊號光70,而往影像感測器210的方向傳遞。FIG. 2A illustrates the situation in which the illumination light having the P polarization direction in FIG. 1 is reflected by the finger, and FIG. 2B illustrates the situation in which the illumination light having the S polarization direction in FIG. 1 is reflected by the finger. Referring to FIGS. 1 , 2A and 2B, the
影像感測器210所感測到的訊號光72與訊號光74的光強度及其比例是有關於手指50與玻璃蓋120的折射率所造成的反射率。圖3為圖1的手指與玻璃蓋的界面對照明光的反射率及穿透率相對於入射此界面的入射角的分佈曲線,其中,Rp
為此界面對照明光62的反射率相對於入射角的分佈曲線,Rs
為此界面對照明光64的反射率相對於入射角的分佈曲線,Tp
為此界面對照明光62的穿透率相對於入射角的分佈曲線,而Ts
為此界面對照明光64的穿透率相對於入射角的分佈曲線。其中,Tp
=1-Rp
,且Ts
=1-Rs
。由圖3可知,在入射角較大處所對應的訊號光72(P偏振光)與訊號光74(S偏振光)的光強度差異越大。由於影像感測器210在越外圈處是收到越大入射角的照明光60所反射成的訊號光70,因此越外圈處所感測到的訊號光72(P偏振光)與訊號光74(S偏振光)的光強度差異越大。The light intensity and the ratio of the
圖4A為圖1的影像感測器所感測到的指紋影像與其所接收到的P偏振光與S偏振光的區域對照圖。請參照圖1、圖2A、圖2B及圖4A,圖4A中,P所在區域的影像亮度主要是由訊號光72(P偏振光)所貢獻,S所在區域的影像亮度主要是由訊號光74(S偏振光)所貢獻,而S+P所在區域的影像亮度主要是由訊號光72(P偏振光)與訊號光74(S偏振光)兩者所共同貢獻。P所在區域因為穿透光多,所以指紋影像的對比度較強;S所在區域因為穿透光少,所以指紋影像的對比度較弱;P所在區域因為反射光少,所以指紋影像的直流值(DC value)低,也就是平均亮度較低;S所在區域因為反射光多,所以指紋影像的直流值高,也就是平均亮度較高。FIG. 4A is a comparison diagram of the fingerprint image sensed by the image sensor of FIG. 1 and the regions of the received P-polarized light and S-polarized light. Please refer to FIG. 1 , FIG. 2A , FIG. 2B and FIG. 4A . In FIG. 4A , the image brightness in the region where P is mainly contributed by the signal light 72 (P polarized light), and the image brightness in the region where S is mainly contributed by the signal light 74 (S-polarized light), and the image brightness in the region where S+P is located is mainly contributed by both the signal light 72 (P-polarized light) and the signal light 74 (S-polarized light). The area where P is located has a lot of penetrating light, so the contrast of the fingerprint image is strong; the area where S is located has less penetrating light, so the contrast of the fingerprint image is weak; the area where P is located has less reflected light, so the DC value of the fingerprint image (DC value) is low, that is, the average brightness is low; the area where S is located has a lot of reflected light, so the DC value of the fingerprint image is high, that is, the average brightness is high.
圖4B為圖1的影像感測器感測到假手指的指紋影像。圖5A為圖1的影像感測器所感測到的真手指的指紋影像於對角線M-N上的平均灰階分佈圖,而圖5B為圖1的影像感測器所感測到的假手指的指紋影像於對角線M-N上的平均灰階分佈圖。在圖5A與圖5B的對角線M-N上的某個畫素的平均灰階是藉由將此畫素附近的幾個畫素的灰階值與此畫素本身的灰階值取平均值而得。比較圖4A與圖4B,以及圖5A與圖5B,可發現真手指的指紋影像於中央的平均亮度較低,而對角線M-N上靠近兩端的平均亮度較高。相反地,假手指的指紋影像於中央的平均亮度較高,而對角線M-N上靠近兩端的平均亮度較低。會造成此種現象的主要原因之一是假手指的折射率與真手指有差異,因此會造成P偏振光與S偏振光的反射率不同,進而造成平均亮度在不同位置的分佈曲線的不同。因此,本發明的實施例便可對此現象加以利用,以產生辨識真手指與假手指的方案,這將於以下內容中詳述。FIG. 4B is a fingerprint image of the fake finger detected by the image sensor of FIG. 1 . FIG. 5A is an average grayscale distribution diagram of the fingerprint image of the real finger on the diagonal line MN sensed by the image sensor of FIG. 1 , and FIG. 5B is a graph of the fake finger sensed by the image sensor of FIG. 1 . The average grayscale distribution of the fingerprint image on the diagonal line MN. The average gray level of a pixel on the diagonal line MN in FIGS. 5A and 5B is obtained by averaging the gray level values of several pixels near the pixel and the gray level value of the pixel itself. And get. Comparing FIGS. 4A and 4B , and FIGS. 5A and 5B , it can be found that the average brightness of the fingerprint image of the real finger is lower in the center, while the average brightness on the diagonal line M-N near both ends is higher. On the contrary, the average brightness of the fingerprint image of the fake finger is higher in the center, and the average brightness near the two ends of the diagonal line M-N is lower. One of the main reasons for this phenomenon is that the refractive index of the fake finger is different from that of the real finger, so the reflectivity of the P-polarized light and the S-polarized light will be different, and the distribution curve of the average brightness at different positions will be different. Therefore, embodiments of the present invention can utilize this phenomenon to generate a solution for identifying real fingers and fake fingers, which will be described in detail below.
圖6為圖1中的控制器處理指紋影像的示意圖。請參照圖1與圖6,在本實施例中,控制器230用以對指紋影像的兩個方向不同的軸線L1與L2上的灰階值與一哈爾特徵作內積(inner product),以獲得一第一結果。具體而言,在本實施例中,控制器先對指紋影像作模糊化處理後,再對經模糊化的指紋影像的兩個方向不同的軸線L1與L2上的灰階值與哈爾特徵作內積。此處的模糊化例如是平均模糊化(mean blur)或高斯模糊化(Gaussian blur),例如將每個畫素的灰階值與此畫素的周圍的幾個畫素的灰階值取平均值,並以此平均值作為此畫素的新灰階值。FIG. 6 is a schematic diagram of the controller in FIG. 1 processing a fingerprint image. Referring to FIG. 1 and FIG. 6 , in this embodiment, the
在本實施例中,哈爾特徵為中間區域C1具有一第一數值而兩側區域C2與C3具有一第二數值的函數,其中第一數值大於第二數值。舉例而言,第一數值為+1,而第二數值為-1。經模糊化後的指紋影像例如具有200×200個像素,而哈爾特徵從左端數來的第1到75個像素所各自具有的數值例如設為-1,第76至125個像素所各自具有的數值例如設為+1,而第126至第200個像素所各自具有的數值例如設為-1。經模糊化後的指紋影像的軸線L1上亦具有200個畫素,而每個畫素具有各自的灰階值,而此灰階值代表經模糊化後的指紋影像的亮度。接著,將軸線L1上的這200個畫素所具有的灰階值依序分別地與哈爾特徵的這200個像素的數值(即-1或+1)相乘,以得到200個積,而後再將這200個積相加,即可獲得上述經內積運算後所得到的數值。In this embodiment, the Haar characteristic is a function of the middle region C1 having a first value and the two side regions C2 and C3 having a second value, wherein the first value is greater than the second value. For example, the first value is +1 and the second value is -1. The blurred fingerprint image has, for example, 200×200 pixels, and the values of the 1st to 75th pixels from the left end of the Haar feature are set to -1, for example, and the 76th to 125th pixels have The value of , for example, is set to +1, and the value of each of the 126th to 200th pixels is set to, for example, -1. The axis L1 of the blurred fingerprint image also has 200 pixels, and each pixel has its own grayscale value, and the grayscale value represents the brightness of the blurred fingerprint image. Next, multiply the grayscale values of the 200 pixels on the axis L1 by the values of the 200 pixels (ie -1 or +1) of the Haar feature in sequence to obtain 200 products, Then add these 200 products together to obtain the value obtained after the above inner product operation.
然後,控制器230將分別對應於軸線L1與軸線L2的兩個內積值相加,以獲得一第一特徵值。比較圖5A、圖5B與圖6可知,軸線L1與L2上的灰階值經由與哈爾特徵作內積運算後,圖5A的真手指的內積值或第一特徵值越小,而圖5B的假手指的內積值或第一特徵值越大,因此所運算出的第一特徵值可用以分辨所感測的是真手指或假手指。Then, the
圖7為圖1中的控制器處理指紋影像的另一示意圖。請參照圖1與圖7,控制器230用以計算指紋影像的一中心區域D2的灰階平均值及在中心區域D2兩側的二周邊區域D1與D3的各自的灰階平均值,以獲得一第二結果。此外,控制器230用以綜合第一結果與第二結果來判斷指紋影像是來自真手指或假手指。FIG. 7 is another schematic diagram of the controller in FIG. 1 processing a fingerprint image. Please refer to FIG. 1 and FIG. 7 , the
具體而言,中心區域D2的灰階平均值為R2,二周邊區域D1與D3的灰階平均值分別為R1與R3,控制器230用以對R1、R2、R3作算術運算,以獲得一第二特徵值。在本實施例中,中心區域D2與該二周邊區域D1與D3排列於指紋影像的一對角線上,而此對角線的方向可依據線偏振片116的穿透軸方向來作決定,也就是選擇真手指與假手指的第二特徵值的區別較大的方向作為此對角線的方向。在一實施例中,上述的算術運算例如為(R2-R1)+(R2-R3),或者上述的算術運算例如為R2-R1-R3,在比較圖5A、圖5B及圖7可知,對於這兩種算數運算所獲得的第二特徵值而言,都是真手指的第二特徵值較小,而假手指的第二特徵值較大,因此所運算出的第二特徵值可用以分辨所感測的是真手指或假手指。Specifically, the grayscale average value of the central area D2 is R2, and the grayscale average values of the two peripheral areas D1 and D3 are respectively R1 and R3. The
圖8為圖1中的控制器處理指紋影像的第一特徵值與第二特徵值的示意圖。請參照圖1與圖8,控制器230用以判斷第一特徵值與第二特徵值所形成的一二維座標(如在圖8的座標平面中的座標)是否落在一預設區域F1或F2內(例如是否落在預設區域F1的橢圓區域內,或是否落在預設區域F2的橢圓區域內),而預設區域F1、預設區域F2或其他區域的選擇可根據所實驗的真手指與假手指的案例來決定。若二維座標落在預設區域F1或F2內,則控制器230判斷指紋影像是來自真手指。若二維座標落在預設區域F1或F2外,則控制器230判斷指紋影像是來自假手指。如此一來,本實施例的屏下式指紋感測裝置100在指紋感測上便可以達到防欺騙的效果,進而提升使用者的個人資料的安全性。FIG. 8 is a schematic diagram of the controller in FIG. 1 processing the first feature value and the second feature value of the fingerprint image. Referring to FIG. 1 and FIG. 8 , the
上述實施例是以綜合第一特徵值與第二特徵值的結果來判斷出真手指與假手指。然而,在其他實施例中,也可以單就第一特徵值來判斷出真手指與假手指,或者是單就第二特徵值來判斷出真手指與假手指。舉例而言,控制器230可以只計算出第一特徵值,並判斷第一特徵值是否落在一預設範圍內。若第一特徵值落在預設範圍內,則判斷指紋影像是來自真手指;若第一特徵值落在預設範圍外,則判斷指紋影像是來自假手指。或者,控制器230可以只計算出第二特徵值,並判斷第二特徵值是否落在一預設範圍內。若第二特徵值落在預設範圍內,則判斷指紋影像是來自真手指;若第二特徵值落在預設範圍外,則判斷指紋影像是來自假手指。In the above embodiment, the real finger and the fake finger are determined based on the result of combining the first feature value and the second feature value. However, in other embodiments, the real finger and the fake finger may also be determined only based on the first feature value, or the real finger and the fake finger may be determined based only on the second feature value. For example, the
在一實施例中,控制器230例如為中央處理單元(central processing unit, CPU)、微處理器(microprocessor)、數位訊號處理器(digital signal processor, DSP)、可程式化控制器、可程式化邏輯裝置(programmable logic device, PLD)或其他類似裝置或這些裝置的組合,本發明並不加以限制。此外,在一實施例中,控制器230的各功能可被實作為多個程式碼。這些程式碼會被儲存在一個記憶體中,由控制器230來執行這些程式碼。或者,在一實施例中,控制器230的各功能可被實作為一或多個電路。本發明並不限制用軟體或硬體的方式來實作控制器230的各功能。In one embodiment, the
綜上所述,在本發明的實施例的屏下式指紋感測裝置中,控制器對指紋影像的兩個方向不同的軸線上的灰階值與一哈爾特徵作內積,及/或計算指紋影像的中心區域的灰階平均值及在中心區域兩側的二個周邊區域的各自的灰階平均值,並藉此判斷指紋影像是來自真手指或假手指。因此,本發明的實施例的屏下式指紋感測裝置在指紋感測上可以達到防欺騙的效果,進而提升使用者的個人資料的安全性。To sum up, in the under-screen fingerprint sensing device of the embodiment of the present invention, the controller takes an inner product of the grayscale values on two axes of the fingerprint image in different directions and a Haar feature, and/or Calculate the grayscale average value of the central area of the fingerprint image and the respective grayscale average values of the two peripheral areas on both sides of the central area, and thereby determine whether the fingerprint image is from a real finger or a fake finger. Therefore, the under-screen fingerprint sensing device of the embodiments of the present invention can achieve the effect of anti-spoofing in fingerprint sensing, thereby enhancing the security of the user's personal data.
50:手指
60、62、64、82、84:照明光
70、72、74:訊號光
100:電子裝置
105:顯示器
112:有機發光二極體顯示面板
114:相位延遲膜
116:線偏振片
120:玻璃蓋
122:上表面
200:屏下式指紋感測裝置
210:影像感測器
220:鏡頭
230:控制器
C1:中間區域
C2、C3:兩側區域
D2:中心區域
D1、D3:周邊區域
F1、F2:預設區域
K0:偏振方向
K1:第一偏振方向
K2:第二偏振方向
L1、L2:軸線50:
圖1為本發明的一實施例的電子裝置的剖面示意圖。 圖2A繪示圖1中具有P偏振方向的照明光被手指反射的情形。 圖2B繪示圖1中具有S偏振方向的照明光被手指反射的情形。 圖3為圖1的手指與玻璃蓋的界面對照明光的反射率及穿透率相對於入射此界面的入射角的分佈曲線。 圖4A為圖1的影像感測器所感測到的指紋影像與其所接收到的P偏振光與S偏振光的區域對照圖。 圖4B為圖1的影像感測器感測到假手指的指紋影像。 圖5A為圖1的影像感測器所感測到的真手指的指紋影像於對角線M-N上的平均灰階分佈圖。 圖5B為圖1的影像感測器所感測到的假手指的指紋影像於對角線M-N上的平均灰階分佈圖。 圖6為圖1中的控制器處理指紋影像的示意圖。 圖7為圖1中的控制器處理指紋影像的另一示意圖。 圖8為圖1中的控制器處理指紋影像的第一特徵值與第二特徵值的示意圖。FIG. 1 is a schematic cross-sectional view of an electronic device according to an embodiment of the present invention. FIG. 2A illustrates the situation in which the illumination light having the P-polarization direction in FIG. 1 is reflected by the finger. FIG. 2B illustrates the situation in which the illumination light having the S polarization direction in FIG. 1 is reflected by the finger. FIG. 3 is a distribution curve of the reflectivity and transmittance of the illuminating light at the interface between the finger and the glass cover of FIG. 1 with respect to the incident angle incident on the interface. FIG. 4A is a comparison diagram of the fingerprint image sensed by the image sensor of FIG. 1 and the regions of the received P-polarized light and S-polarized light. FIG. 4B is a fingerprint image of the fake finger detected by the image sensor of FIG. 1 . FIG. 5A is an average grayscale distribution diagram on the diagonal line M-N of the fingerprint image of the real finger sensed by the image sensor of FIG. 1 . FIG. 5B is an average grayscale distribution diagram on the diagonal line M-N of the fingerprint image of the fake finger sensed by the image sensor of FIG. 1 . FIG. 6 is a schematic diagram of the controller in FIG. 1 processing a fingerprint image. FIG. 7 is another schematic diagram of the controller in FIG. 1 processing a fingerprint image. FIG. 8 is a schematic diagram of the controller in FIG. 1 processing the first feature value and the second feature value of the fingerprint image.
50:手指50: Fingers
60:照明光60: Illumination light
70:訊號光70: Signal light
100:電子裝置100: Electronics
105:顯示器105: Display
112:有機發光二極體顯示面板112: Organic Light Emitting Diode Display Panel
114:相位延遲膜114: Phase retardation film
116:線偏振片116: Linear polarizer
120:玻璃蓋120: glass cover
122:上表面122: upper surface
200:屏下式指紋感測裝置200: Under-screen fingerprint sensing device
210:影像感測器210: Image Sensor
220:鏡頭220: Lens
230:控制器230: Controller
K0:偏振方向K0: Polarization direction
Claims (20)
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US202063035003P | 2020-06-05 | 2020-06-05 | |
US63/035,003 | 2020-06-05 | ||
US202063035841P | 2020-06-08 | 2020-06-08 | |
US63/035,841 | 2020-06-08 |
Publications (2)
Publication Number | Publication Date |
---|---|
TW202147170A true TW202147170A (en) | 2021-12-16 |
TWI762053B TWI762053B (en) | 2022-04-21 |
Family
ID=74426035
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW109215752U TWM608710U (en) | 2020-06-05 | 2020-11-30 | Under-screen fingerprint sensing device |
TW109141947A TWI762053B (en) | 2020-06-05 | 2020-11-30 | Under-screen fingerprint sensing device |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW109215752U TWM608710U (en) | 2020-06-05 | 2020-11-30 | Under-screen fingerprint sensing device |
Country Status (3)
Country | Link |
---|---|
CN (1) | CN112287903A (en) |
TW (2) | TWM608710U (en) |
WO (1) | WO2021243986A1 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWM608710U (en) * | 2020-06-05 | 2021-03-01 | 神盾股份有限公司 | Under-screen fingerprint sensing device |
Family Cites Families (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW200719242A (en) * | 2005-11-03 | 2007-05-16 | Wison Technology Corp | Method of determining fingerprint of living body |
US8098906B2 (en) * | 2006-10-10 | 2012-01-17 | West Virginia University Research Corp., Wvu Office Of Technology Transfer & Wvu Business Incubator | Regional fingerprint liveness detection systems and methods |
CN101789075B (en) * | 2010-01-26 | 2012-09-26 | 哈尔滨工程大学 | Finger vein identifying method based on characteristic value normalization and bidirectional weighting |
CN102446268A (en) * | 2010-09-30 | 2012-05-09 | 神盾股份有限公司 | Fingerprint anti-counterfeit device and method thereof |
MX346218B (en) * | 2012-09-05 | 2017-03-09 | Element Inc | Biometric authentication in connection with camera-equipped devices. |
TWI519993B (en) * | 2013-12-17 | 2016-02-01 | 神盾股份有限公司 | Fake finger discrimination device and method |
US9633269B2 (en) * | 2014-09-05 | 2017-04-25 | Qualcomm Incorporated | Image-based liveness detection for ultrasonic fingerprints |
CN104794440B (en) * | 2015-04-15 | 2018-03-13 | 杭州景联文科技有限公司 | A kind of false fingerprint detection method based on the multiple dimensioned LBP of more piecemeals |
CN108304759A (en) * | 2017-01-11 | 2018-07-20 | 神盾股份有限公司 | Identify the method and electronic device of finger |
TWM570473U (en) * | 2018-07-03 | 2018-11-21 | 金佶科技股份有限公司 | Image capturing module |
CN109740575A (en) * | 2019-01-30 | 2019-05-10 | 北京一维大成科技有限公司 | A kind of method, apparatus of authentication, computer-readable medium and equipment |
CN110334694B (en) * | 2019-07-18 | 2023-05-09 | 上海菲戈恩微电子科技有限公司 | Under-screen optical fingerprint anti-attack method based on polarized light |
WO2021072768A1 (en) * | 2019-10-18 | 2021-04-22 | 深圳市汇顶科技股份有限公司 | Fingerprint recognition device and electronic apparatus |
TWM608710U (en) * | 2020-06-05 | 2021-03-01 | 神盾股份有限公司 | Under-screen fingerprint sensing device |
-
2020
- 2020-11-30 TW TW109215752U patent/TWM608710U/en unknown
- 2020-11-30 WO PCT/CN2020/132935 patent/WO2021243986A1/en active Application Filing
- 2020-11-30 CN CN202011379861.7A patent/CN112287903A/en active Pending
- 2020-11-30 TW TW109141947A patent/TWI762053B/en not_active IP Right Cessation
Also Published As
Publication number | Publication date |
---|---|
TWI762053B (en) | 2022-04-21 |
WO2021243986A1 (en) | 2021-12-09 |
TWM608710U (en) | 2021-03-01 |
CN112287903A (en) | 2021-01-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109325400B (en) | Display and electronic device for identifying fingerprint | |
US9542045B2 (en) | Detecting and tracking touch on an illuminated surface using a mean-subtracted image | |
US11232546B2 (en) | Texture detection method, texture image compensation method and device, and electronic device | |
US10116868B2 (en) | Display-integrated user-classification, security and fingerprint system | |
US20180349721A1 (en) | Biometric object spoof detection based on image intensity variations | |
US9436864B2 (en) | Electronic device performing finger biometric pre-matching and related methods | |
US10599933B2 (en) | Biometric image capturing apparatus and biometric image capturing method | |
TWI592880B (en) | Optical apparatus and a method for identifying an object | |
TWI470478B (en) | Virtual keyboard of an electronic device and a data inputting method therefor | |
TWI762053B (en) | Under-screen fingerprint sensing device | |
WO2017205123A1 (en) | Device having display integrated visible and infrared light source for user authentication | |
US9690430B2 (en) | Touch detection apparatus, touch detection method and recording medium | |
US9389702B2 (en) | Input association | |
US9377900B1 (en) | Optical touch sensor | |
US10521052B2 (en) | 3D interactive system | |
WO2020088156A1 (en) | Under-screen fingerprint module, electronic device and fingerprint image processing method | |
TWI767285B (en) | Fingerprint identification device, electronic device for identificating fingerprint image and fingerprint identification method | |
JP6861835B2 (en) | Touch panel device | |
TW202244699A (en) | Integrating optical fingerprinting into a front-facing smartphone camera | |
TWI658411B (en) | Non-directional finger palm print recognition method and non-directional finger palm print data establishment method | |
US20240085172A1 (en) | Device and method for sensing depth | |
TW201812247A (en) | Tapping detecting device, tapping detecting method and smart projecting system using the same |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
MM4A | Annulment or lapse of patent due to non-payment of fees |