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HandNet: Identification Based on Hand Images Using Deep Learning Methods

Biometric identification is the technology that differentiates individuals by body parts or behavioral characteristics. Hand has been proved to be a successful biometric for verification and identification because of the rich features such as fingerprint, palmprint, dorsal vein, etc. This paper presents a system for identifying individuals based on their hand images. Firstly, after image preprocessing with guided filter and CLAHE method, hand images taken under visible light and near-infrared (NIR) light were normalized. Secondly, a convolutional neural network structure was designed and trained on a large dataset. Using hand images as the input of the network, different depth features were extracted, including the feature from the fusion layer. Thirdly, SVM classifiers were adopted to get the classification results. A fusion strategy was used to make use of different SVM classifiers. The proposed algorithm was tested on different datasets and the experimental results showed that high accuracy can be obtained from the fusion of features. It shows that the hand image is a strong biometric for verification and identification.