Accurate Face Recognition on Highly Compressed Samples

Compressive sensing is an emerging field for lowdimensional data acquisition. Samples are acquired in the compressed domain and utilized for signal reconstruction or as input features for a classifier. In this work, hardware-aware face recognition using compressed samples was investigated. A linear support vector machine (SVM) classifier was exploited with compressed samples as input features; Faces can be reliably recognized with high average accuracy (up to 99%). To assess the robustness of the proposed scheme, three image datasets covering different facial and illumination conditions were analyzed. Random (binary) and structured (Haar-transform-based) measurement matrices were employed for generating compressed samples. For one of the datasets, Extended Yale B, and using a random binary measurement matrix, the proposed scheme achieved 82% accuracy from as few as 15 compressed samples, which means a 1/20480 sensing ratio. Accuracy and compression are also remarkably high with respect to the state-of-the-art for the other two datasets.