Device to extract biometric feature vector, method to extract biometric feature vector and program to extract biometric feature vector
A feature vector and extraction device technology, applied in the field of biological feature vector extraction programs, can solve the problems of reduced comparison accuracy, variation, and reduced reproducibility of biological feature information, and achieves the effect of improving reproducibility
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Embodiment 1
[0030] FIG. 1(a) is a block diagram illustrating the hardware configuration of the living body feature vector extraction device 100 according to the first embodiment. figure 1 (b) is a schematic diagram of a biosensor 105 described later. refer to figure 1 (a), the biological feature vector extraction device 100 includes a CPU 101 , a RAM 102 , a storage device 103 , a display device 104 , a biological sensor 105 and the like. These respective devices are connected via a bus or the like.
[0031] The CPU (Central Processing Unit: Central Processing Unit) 101 is a central processing unit. CPU 101 includes one or more cores. RAM (Random Access Memory: Random Access Memory) 102 is a volatile memory that temporarily stores programs executed by CPU 101 , data processed by CPU 101 , and the like.
[0032] The storage device 103 is a nonvolatile storage device. As the storage device 103 , for example, a solid state drive (SSD) such as a ROM (Read Only Memory) or a flash memory, ...
Embodiment 2
[0049] In Example 1, for N points of interest f n , searching for the feature point with the smallest distance, and determining the small area image according to each feature point, but not limited thereto. In embodiment 2, each small area image is generated based on the brightness center of gravity of the vein image.
[0050] Figure 7 It is a flowchart showing an example of the generation process of the small region image according to this embodiment. refer to Figure 7 Then, the small area image generation unit 11 calculates the luminance center of gravity G(p, q) of the vein image acquired by the biometric sensor 105 (step S21). The center of gravity of luminance can be calculated according to the following formula, for example. i and j represent the coordinates of pixels, and P(i, j) represents the luminance value of each pixel.
[0051] 【Number 1】
[0052] G ( p , q ) =...
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