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Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance

Researchers have conducted many studies on human atten- tions and their eye gaze patterns for face recognition (FR), hoping to inspire new ideas to develop computer vision (CV) algorithms which perform like or even better than human. Yet, while these studies have been performed on still face images, human attention in natural videos have not been investigated. In this work, we conducted a human psycho- physics experiment to study the human FR performance, attention and analyze eye gaze patterns on challenging natural videos. We compared human and machine FR performance in natural videos by applying a state-of-the-art deep convolu- tional neural network (dCNN). Our ﬁndings show a signiﬁ-cant gap between machine and human on performance, espe- cially for humans who achieve higher FR performance (high-performers). Learning from the cognitive capabilities of hu- mans, in particular the high-performers, may aid in reducing this gap. Hence, we investigated persons’ attentions to unfa- miliar faces in a challenging face recognition task, and studied their eye gaze patterns. We propose a new and effective computational approach for eye gaze pattern analysis on natural videos, which produces reliable results and reduces the time and manual efforts needed. Our ﬁndings reveal that humans in general attend more to the regions around the center of the face than the other facial regions. The high-performers in particular reveal attention to the lower part of the face in addition to the centre regions.