Jarullah, Thoalfeqar G. ORCID: https://orcid.org/0009-0000-2548-9648, Mohammad, Ahmad Saeed ORCID: https://orcid.org/0000-0001-6141-2605, Al-Kaltakchi, Musab T. S. ORCID: https://orcid.org/0000-0001-5542-9144 and Alshehabi Al-ani, Jabir ORCID: https://orcid.org/0000-0002-0553-2538 (2025) Intelligent Face Recognition: Comprehensive Feature Extraction Methods for Holistic Face Analysis and Modalities. Signals, 6 (3). p. 49.

Preview

Text signals-06-00049-v2.pdf - Published Version Available under License Creative Commons Attribution. Download (13MB) | Preview

Abstract

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier.

Item Type:	Article
Status:	Published
DOI:	10.3390/signals6030049
School/Department:	York Business School
URI:	https://ray.yorksj.ac.uk/id/eprint/12996

University Staff: Request a correction | RaY Editors: Update this record