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Attentiveness, identification and recognition of a human face in a crowd play a starring role in the perspective of visual surveillance. Human vision system is not attending, identifying and recognizing all the faces with the same perception. It is biased towards some faces in the crowd due to their higher relative visual saliency and segment wise perception with respect to the surroundings faces. Using different computer vision-based techniques enormous researches have been carried out on attention, recognition and identification of the human face in context of different applications. This paper proposes a novel technique to explain and analyse how the attention, identification and recognition of a face in the crowd guided through segmentation and their relative visual saliency. The proposed method is stretched out the solution, using the concept of segmentation and relative visual saliency which is evaluated on the intensity values and respective spatial distance of the faces. [ABSTRACT FROM AUTHOR]

In unconstrained environments, it encounters a number of challenges when considering handcrafted features for face recognition, including changes in pose, illumination and facial expression and plastic surgery variations, look-alike faces and selfie images. As majority of the published works on local descriptors are based on the relationship between the centre pixel and neighbourhood pixels at different radial widths, by including the relationship between different neighbourhood pixels of a target pixel with it can facilitate the decoupling of the discriminatory features further. To exploit such relationships and to mitigate the challenges, the proposed study reports a novel local descriptor called Local Directional Octa Pattern (LDOP) that makes use of derivative operation and three well-known metrics, namely, histogram intersection, K-nearest neighbour and Euclidean distance for face identification. Rigorous experiments have been conducted on six benchmark face databases, namely, Extended Yale Face B, Labeled Faces in the Wild, Plastic Surgery, Look-alike, UMDAA-02-FD and ORL, confirming the superiority of the proposed local descriptor over state-of-the-art descriptors with accuracies of 97.37%, 49.09%, 75.47%, 31.2% and 93.31% at Rank–10 and 100% at Rank-5 determined on the databases, respectively. [ABSTRACT FROM AUTHOR]