Enhancing Face Recognition Performance with Multispectral Imaging and Machine Learning: Comparison from Sift and Sift-Freak Feature Extraction

Abstract

The development of modern societies faces many security and identification challenges. To meet this expectation, computer vision offers biometric solutions. Much research in recent years has focused on face recognition. Traditional facial recognition that uses color images has had many shortcomings, such as variation in illumination, smoke, rain, disguise, face concealment, makeup, etc. Light-insensitive infrared (IR) imaging is presented as an alternative to facial recognition in the visible to overcome the shortcomings of uncontrolled environments. However, IR also has weaknesses, such as facial occlusion by glasses, variation in body temperature, perfusion, etc. This paper proposes a new facial recognition architecture that uses several classification algorithms, detectors, and feature descriptors in multispectral imaging. A combination of SIFT and FREAK, feature extraction tools, was associated with classification algorithms such as SVM, logistic regression, and Random forest to conduct this study. Several experiments were made to evaluate the performance of the proposed recognition system. The validation process of the proposed multispectral face recognition method involved several important steps. First, experiments were carried out on visible and infrared spectrum images to measure the recognition system's performance. These experiments made it possible to compare the recognition performances between these two types of images. Then, fuse visible and infrared images were used to assess multispectral facial recognition. The goal was to maximize each spectrum's advantages while minimizing their disadvantages. Metrics were evaluated to measure the accuracy of the multispectral face recognition method. The performances were compared with classical facial recognition methods, such as facial recognition based on the visible spectrum or infrared imagery alone. The results showed that the proposed multispectral facial recognition method performed better than traditional methods, reaching a facial recognition score ranging from 76% to 95% in the IRIS database.