Face Detection on Embedded Systems

Over recent years automated face detection and recognition (FDR) have gained significant attention from the commercial and research sectors. This paper presents an embedded face detection solution aimed at addressing the real-time image processing requirements within a wide range of applications. As face detection is a computationally intensive task, an embedded solution would give rise to opportunities for discrete economical devices that could be applied and integrated into a vast majority of applications. This work focuses on the use of FPGAs as the embedded prototyping technology where the thread of execution is carried out on an embedded soft-core processor. Custom instructions have been utilized as a means of applying software/hardware partitioning through which the computational bottlenecks are moved to hardware. A speedup by a factor of 110 was achieved from employing custom instructions and software optimizations

Design Methodology for Face Detection Acceleration

A design methodology to accelerate the face detection for embedded systems is described, starting from high level (algorithm optimization) and ending with low level (software and hardware codesign) by addressing the issues and the design decisions made at each level based on the performance measurements and system limitations. The implemented embedded face detection system consumes very little power compared with the traditional PC software implementations while maintaining the same detection accuracy. The proposed face detection acceleration methodology is suitable for real time applications.Ministerio español de Ciencia y Tecnología TEC2011-24319Junta de Andalucía FEDER P08-TIC-0367

Embedded face detection application based on local binary patterns

Comunicación presentada al "HPCC", "ICESS" y "CSS" IEEE International Conference on Embedded Software and Systems, ICESS International Symposium on Cyberspace Safety and Security, CSSIn computer vision during the recent years a new paradigm for object detection has stimulated researchers and designers interest. The foundation of this new paradigm is the Local Binary Pattern (LBP) which is a nonparametric operator that efficiently extracts the features of local structures in images. This communication describes a software embedded implementation of LBP based algorithm for object detection, in particular targeting frontal face detection

Comparative analysis of selected facial recognition algorithms

Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined. Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition

Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

A characterization of visual feature recognition

technical reportNatural human interfaces are a key to realizing the dream of ubiquitous computing. This implies that embedded systems must be capable of sophisticated perception tasks. This paper analyzes the nature of a visual feature recognition workload. Visual feature recognition is a key component of a number of important applications, e.g. gesture based interfaces, lip tracking to augment speech recognition, smart cameras, automated surveillance systems, robotic vision, etc. Given the power sensitive nature of the embedded space and the natural conflict between low-power and high-performance implementations, a precise understanding of these algorithms is an important step developing efficient visual feature recognition applications for the embedded space. In particular, this work analyzes the performance characteristics of flesh toning, face detection and face recognition codes based on well known algorithms. We also show how the problem can be decomposed into a pipeline of filters that have efficient implementations as stream processors

Fully Quantized Always-on Face Detector Considering Mobile Image Sensors

Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors are essential in various practical applications, such as energy-efficient mobile phones and surveillance systems with always-on capabilities. One noteworthy limitation is the absence of suitable face detectors for the always-on scenario, a crucial aspect of image sensor-level applications. These detectors must operate directly with sensor RAW data before the image signal processor (ISP) takes over. This gap poses a significant challenge in achieving optimal performance in such scenarios. Further research and development are necessary to bridge this gap and fully leverage the potential of iCIS applications. In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications. To achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs, simulating always-on face detection processed "before" the ISP chain. Our approach employs ternary (-1, 0, 1) weights for potential implementations in image sensors, resulting in a relatively simple network architecture with shallow layers and extremely low-bitwidth. Our method demonstrates reasonable face detection performance and excellent efficiency in simulation studies, offering promising possibilities for practical always-on face detectors in real-world applications.Comment: Accepted to ICCV 2023 Workshop on Low-Bit Quantized Neural Networks (LBQNN), Ora

Real-time acquisition of multi-view face images to support robust face recognition using a wireless camera network

Recent terror attacks, intrusion attempts and criminal activities have necessitated a transition to modern biometric systems that are capable of identifying suspects in real time. But real-time biometrics is challenging given the computationally intensive nature of video processing and the potential occlusions and variations in pose of a subject in an unconstrained environment. The objective of this dissertation is to utilize the robustness and parallel computational abilities of a distributed camera network for fast and robust face recognition.;In order to support face recognition using a camera network, a collaborative middle-ware service is designed that enables the rapid extraction of multi-view face images of multiple subjects moving through a region. This service exploits the epipolar geometry between cameras to speed up multi view face detection rates. By quickly detecting face images within the network, labeling the pose of each face image, filtering them based on their suitability of recognition and transmitting only the resultant images to a base station for recognition, both the required network bandwidth and centralized processing overhead are reduced. The performance of the face image acquisition system is evaluated using an embedded camera network that is deployed in indoor environments that mimic walkways in public places. The relevance of the acquired images for recognition is evaluated by using a commercial software for matching acquired probe images. The experimental results demonstrate significant improvement in face recognition system performance over traditional systems as well as increase in multi-view face detection rate over purely image processing based approaches

Implementation of Eye Detection Using Dual Camera on The Emebedded System

This paper presents an implementation of eye detection on the embedded system. Two camera systems based-on the low cost Raspberry Pi modules are employed. To speed up the process, the proposed system implements the face detection technique and the eye detection technique on two camera modules separately. The face detection module detects the bounding box of face and sends the coordinates to the eye detection module via a serial communication. In the eye detection module, the eye is searched on a limited area defined by the face’s bounding box. The popular Viola-Jones object detection is employed in the face detection module. Three eye detection techniques consist of the Viola-Jones method, the eye-map method, and the Hough circle transform method are implemented and evaluated in the eye detection module. The best result is obtained by the Hough circle transform method, where the frame rate of 30.020 fps, the true positive rate of 0.869, and the precision of 0.824 is achieved. Keywords: Face detection, Eye Detection, Viola-Jones, Eye-map, Hough transform, Raspberry Pi, Dual camer