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Since the last two decades, face recognition has been a significant area in the fields of biometrics and machine learning that uses the different appearances and geometric-based facial features to recognize the living substances present in a frame or video. In this article, we have proposed an inceptio-residual convolutional neural network inspired by the ResNet architecture for deep facial feature extraction and recognition. Besides that, some of the well-known lite or moderately dense and highly accurate ImageNet benchmarked convolutional neural networks, such as VGG-19 BN, ResNet 18/34/50, SE-ResNet50, ResNeXt50, Inception V2/V3/V4, Xception50, and DualPathNet68, have been utilized for face recognition. In conjunction with that, the proposed one was tested along those above-standard networks with respect to various performance metrics. Based on the results, we concluded that the proposed Incepto-residual convolutional neural network has performed better than the ImageNet benchmarked Convolutional Neural Networks.

A lightweight convolutional neural network built on inceptio-residual and reduction modules for deep facial recognition in realistic conditions