An LDOP approach for face identification under unconstrained scenarios

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Issue Date
2023-03-01
Authors
Datta Rakshit, Rinku
Rattani, Ajita
Kisku, Dakshina Ranjan
Advisor
Citation

Rinku Datta Rakshit, Ajita Rattani & Dakshina Ranjan Kisku (2023) An LDOP approach for face identification under unconstrained scenarios, Journal of Experimental & Theoretical Artificial Intelligence, DOI: 10.1080/0952813X.2023.2183274

Abstract

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.

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