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Fingerphoto images captured using a smartphone are successfully used toverify the individuals that have enabled several applications. This workpresents a novel algorithm for fingerphoto verification using a nested residualblock: Finger-NestNet. The proposed Finger-NestNet architecture is designedwith three consecutive convolution blocks followed by a series of nestedresidual blocks to achieve reliable fingerphoto verification. This paper alsopresents the interpretability of the proposed method using four differentvisualization techniques that can shed light on the critical regions in thefingerphoto biometrics that can contribute to the reliable verificationperformance of the proposed method. Extensive experiments are performed on thefingerphoto dataset comprised of 196 unique fingers collected from 52 uniquedata subjects using an iPhone6S. Experimental results indicate the improvedverification of the proposed method compared to six different existing methodswith EER = 1.15%.

Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network