Abstract
Image based face identification systems have attained optimal performance. However, the design of such systems often involves some issues related to extreme light conditions and privacy protection, among others. Since several years, Face Identification (FI) based on thermal images using deep neural networks (DNN) has received significant attention. Yet, the majority of the FI systems developed through DNN’s need huge computational power; those systems are not suitable for the devices with memory limitations. In this paper, we proposed a new CNN framework based on depthwise separable convolutions for real-time face identification for low memory vision applications. The lack of publicly available thermal datasets makes very hard the research and developing of new techniques. In this work, we further present a new large-scale thermal face database called “ST_UNICT_Thermal_Face”. As per our analysis, the evaluation of the learnt model using the data obtained in the single-day (without temporal variations), it might not stable over time. One of the main reasons behind the development of this database for the real-time evaluation of the proposed model depends on the fact that most thermal face identification systems are not stable over time and climate due to insufficient time data. The evaluation results exhibit that the proposed framework is suitable for the devices having limited memory and which is stable over time and different indoor environmental conditions.
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Devaram, R.R., Ortis, A., Battiato, S., Bruna, A.R., Tomaselli, V. (2021). Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_44
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