Recognizing faces prone to occlusions and common variations using optimal face subgraphs

Lahasan, Badr; Venkat, Ibrahim; Al-Betar, Mohammed Azmi; Lutfi, Syaheerah Lebai; Wilde, Philippe De

doi:10.1016/j.amc.2016.02.047

Cited by 4 publications

(1 citation statement)

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“…The limitation of the EBGM is that one needs to rely on the model's manual ground truth for landmark selection at the initial recognition stage. Lahasan et al [30] proposed a method to overcome this shortcoming by posing the EBGM as an optimization problem by using harmony search (HS) to find the optimal facial landmarks using the manual method. Scale-invariant feature transform SIFT was proposed by Lowe [32][33].…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

Barreto

Sarvaiya

Patnaik³

2022

Adv. technol. innov.

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For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy.

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Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%