Real-time embedded eye detection system. (15th May 2022)

• Record Type: Journal Article
• Authors: Ruiz-Beltrán, Camilo A.; Romero-Garcés, Adrián; González, Martín; Pedraza, Antonio Sánchez; Rodríguez-Fernández, Juan A.; Bandera, Antonio
• Is Part Of: Expert systems with applications. Volume 194(2022)
• Publication Date: 2022-05-15
• Subjects: Eye detection -- Viola–Jones algorithm -- All programmable System-on-Chip; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116505
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The detection of a person's eyes is a basic task in applications as important as iris recognition in biometric identification or fatigue detection in driving assistance systems. Current commercial and research systems use software frameworks that require a dedicated computer, whose power consumption, size and price are significantly large. This paper presents a hardware-based embedded solution for eye detection in real-time. From an algorithmic point-of-view, the popular Viola–Jones approach has been redesigned to enable highly parallel, single-pass image-processing implementation. Synthesized and implemented in an All-Programmable System-on-Chip (AP SoC), this proposal allows us to process more than 88 frames per second (fps), taking the classifier less than 2 ms per image. Experimental validation has been successfully addressed in an iris recognition system that works with walking subjects. In this case, the prototype module includes a CMOS digital imaging sensor providing 16 Mpixels images, and it outputs a stream of detected eyes as 640 × 480 images. Experiments for determining the accuracy of the proposed system in terms of eye detection are performed in the CASIA-Iris-distance V4 database. Significantly, they show that the accuracy in terms of eye detection is 100%. Highlights: A single-pass image-processing redesign of the Viola–Jones approach is proposed. The classifier core can process more than 750 fps. An accuracy rate of 100% is obtained using the CASIA-Iris-distance V4 database. The proposal is integrated into a framework for iris identification on motion.