Face Recognition in Machine Learning: A Framework for Dimensionality Reduction Algorithms

• Published in: International journal of advanced networking and applications Vol. 14; no. 2; pp. 5396 - 5407
• Main Author: Kabanda, Gabriel
• Format: Journal Article
• Language: English
• Published: Eswar Publications 01.09.2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Big Data; Classification; Clustering; Cybersecurity; Data analysis; Data mining; Digital imaging; Epistemology; Face recognition; Facial recognition technology; Feature selection; Internet of Things; Localization; Machine learning; Object recognition; Performance evaluation; Principal components analysis; Reduction; Scientific papers; Software
• ISSN: 0975-0282; 0975-0290; 0975-0282
• DOI: 10.35444/IJANA.2022.14211

A facial recognition system matches a human face from a digital image or a video frame against an authentic repository of faces or Eigenfaces subject to algorithmic performance and detection accuracy. Dimensionality reduction is a type of unsupervised learning for which input is images of higher-dimensional data and these images are represented with a lower-dimensional space. The purpose of the research paper is to evaluate the performance of Dimensionality Reduction algorithms for face recognition using different approaches of Machine Learning (ML). The research uses the Interpretivist Paradigm characterised by a subjectivist epistemology, relativist ontology, naturalist methodology, and a balanced axiology. The quantitative methodology with an experimental research design was used. The results of the experiment show that only selecting the top M eigenfaces reduces the dimensionality of the data, and that too few eigenfaces results in too much information loss, and hence less discrimination between faces. With increasing dimensionality, the amount of training instances needed rises exponentially (i.e., kd). The performance of the Dimensionality Reduction Algorithm is benchmarked against the Clustering, Bayesian, Genetic, Reinforcement Q-Learning and Reinforcement A3C Algorithms. The outcome of the research makes significant value-adding contributions to the future of advances in Big Data Analytics and ML.