A Transfer Learning Based Approach for Human Foot Image Classification

A human footprint has all the properties like fingerprint which are required for authentication. It has all the desirable properties including uniqueness, universality, and invariability, yet not very widely used for authentication purpose. Hence exploring this novel biometric system is very important. The article explores the singularity available in a footprint image, it is further utilized while performing footprint classification. The main application of footprint classification is to reduce the sample size while performing the authentication. Four different singularities are manually marked on the collected dataset, furthermore, it is trained using different CNN models VGGNet16 and ResNet. A VGGNet-16 model has 16 convolution layer of uniform architecture,with 138 million parameters per layer, while a ResNet50 has 152 layers which is 8×8 times deeper than VGGNet-16. These models are tested on poor-quality dataset (most of the time we are not able to collect clear footprint images), it provides the accuracy of 80%.