Convolutional Neural Network based Eye Recognition from Distantly Acquired Face Images for Human Identification

Abstract

Eye image recognition from a face image acquired at a distant is a promising physical biometric technique to use for human identification. This contemporary field of research depends on image preprocessing, feature extraction, and reliable classification techniques. In this work, we separate eye images from an image of the entire face of a subject and then extract features from these eye images utilizing a convolutional neural network (CNN) model. In general, CNN models convolve images in different layers to extract effective features and then use the softmax function to produce a probability output in the final layer. In our approach, we use CNN features and a kernel extreme learning machine (KELM) classifier instead of softmax to modify the original CNN model. The modified CNN-KELM model has been verified using the publicly available CASIA.v4 distance image database. The experimental results demonstrate that our proposed approach obtains a satisfactory recognition result when compared with several current state-of-the-art human identification approaches.