In this fast-paced technology-driven world, the blasting usage of the internet and the digital transformation have embarked great impacts across all possible domains viz. business, commerce, marketing, governance, defence, and what not? Digital operations entail easy, fast, and secure modes to reveal the user’s identity. This led to the innovative emergence of Biometrics which refers to automated personal recognition based on intrinsic and unique features (biological and/or behavioural). Foot biometrics, like other prevailing biometrics, unleashes the distinguishing capability of human footprints to identify or authenticate a person. This article presents two tracks of the implementation of the latest technology of deep neural networks for personal recognition using human footprints. The first method uses a pretrained VGG19 CNN model to drill down deep features followed by classification. This approach examines four classifiers namely, Gradient Booster, Random Forest, KNN, and ANN, to choose the bestsuited classifier. The second approach employs transfer learning using RESNET-50 deep learning model to perform automatic feature extraction and classification both for footprint recognition. Experimental results reveal that the first method achieved an accuracy of 99.5% and 97.73% recognition accuracy was attained by implementing the second method, indicating the robustness of the proposed system for foot biometrics.