Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7641
• Main Authors: Caba, Julián, Barba, Jesús, Rincón, Fernando, de la Torre, José Antonio, Escolar, Soledad, López, Juan Carlos
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.10.2022; MDPI
• Subjects: Algorithms; biometrics; Biometry; Computer vision; Coronaviruses; COVID-19; Datasets; Deep learning; Disease transmission; Facial Recognition; Facial recognition technology; hyperspectral compression; hyperspectral imaging; Machine vision; Masks; Pandemics; Sensors; Support Vector Machine; SVM; biometrics; SVM; facial recognition; hyperspectral imaging; hyperspectral compression; computer vision
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197641

Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.