Accurate Face Recognition on Highly Compressed Samples A. Khan, J. Fernández-Berni and R. Carmona-Galán Conference · International Conference on Signal Image Technology and Internet Based Systems SITIS 2022 abstract
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.
An Architecture for On-Chip Face Recognition in a Compressive Image Sensor A. Khan, J. Fernandez-Berni and R. Carmona-Galan Conference · IEEE International System-On-Chip Conference SOCC 2022 abstract
Compressive sensing has been widely explored for image reconstruction; however, compressed samples themselves contain relevant signal information and, hence, could be exploited for inference purposes. Many previous studies investigated image recognition on compressed samples, but few of them considered on-chip realization. In this study, an architecture for face recognition that exploits compressed samples was investigated. We found that by using a linear support-vector-machine (SVM) classifier with such samples as input, faces can be recognized with higher performance than in previous works for the level of compression expected in a system-on-chip (SoC) implementation. To compare our results with those of existing works, a figure of merit is proposed. Three image datasets were analyzed to cover diverse aspects, such as variation in illumination, different poses, aging effect, and changing backgrounds. The compression scheme shows robustness under this variety of input signals. A pseudo-diagonal measurement matrix and an architecture suited for in-sensor on-chip implementation are proposed. The resulting inference framework is suitable for an SoC implementation encompassing an image sensor to perform CS acquisition and a DSP to run the SVM.
High-Level Inference On-Chip Enabled by Compressed Sensing A. Khan, J. Fernández-Berni and R. Carmona-Galán Conference · Conference on Design of Circuits and Integrated Systems DCIS 2021 abstract
Compressed sensing (CS) proposes an alternative approach for image acquisition. The traditional method implies acquiring an image and then compressing it. In CS, the image is directly acquired in the compressed domain. Learning can be performed in the compressed domain, known as compressed learning. When it comes to conducting inference on a CS-based image, compressed learning eliminates the need of reconstructing the compressively acquired image. Thus, in this work, compressed learning is suggested for high-level inference. Our ultimate goal is to implement a CMOS smart sensor-processor chip exploiting CS for on-chip inference based on the hypotheses that working in the compressed domain allows implementing high-level inference under severe restrictions on computational and power resources.
