Analyzing ConvNets Depth for Deep Face Recognition
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Deep convolutional neural networks are becoming increasingly popular in large-scale image recognition, classification, localization, and detection. In this paper, the performance of state-of-the-art convolution neural networks (ConvNets) models of the ImageNet challenge (ILSVRC), namely VGG16, VGG19, OverFeat, ResNet50, and Inception-v3 which achieved top-5 error rates up to 4.2% are analyzed in the context of face recognition. Instead of using handcrafted feature extraction techniques which requires a domain-level understanding, ConvNets have the advantages of automatically learning complex features, more training time, and less evaluation time. These models are benchmarked on AR and Extended Yale B face dataset with five performance metrics, namely Precision, Recall, F1-score, Rank-1 accuracy, and Rank-5 accuracy. It is found that GoogleNet ConvNets model with Inception-v3 architecture outperforms than other four architectures with a Rank-1 accuracy of 98.46% on AR face dataset and 97.94% accuracy on Extended Yale B face dataset. It confirms that deep CNN architectures are suitable for real-time face recognition in the future.
KeywordsDeep learning Face recognition Convolutional neural networks Computer vision
This research project is supported by DAE-BRNS, Department of Atomic Energy, Government of India. The authors would like to extend their sincere thanks to DAE-BRNS for their support.
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