An Efficient Method for Face Quality Assessment on the Edge

Okcu, Sefa Burak; Özkalaycı, Burak; Çığla, Cevahir

doi:10.1007/978-3-030-68238-5_5

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…S.B. Okcu et al 14 delineate an efficient approach for Face Quality Assessment (FQA) through the incorporation of face quality score computation alongside a face landmark detection network. As expounded in the paper and exemplified in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…To construct our machine learning model, we took inspiration from the research conducted by S. B. Okcu et al, 14 which presents an effective approach for assessing face quality by integrating face quality score computation along with face landmark detection. Utilizing a comparable strategy, we enhanced our ML model to not only extract facial coordinates but also generate face quality scores and facial landmarks by integrating additional layers into the model architecture.…”

Section: Face Detection Subsystemmentioning

confidence: 99%

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Real-time face recognition system at the edge

Ozen,

Alim,

Okcu

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Face recognition (FR) technology has gained widespread popularity due to its diverse utility and broad range of applications. It is extensively used in various domains, including information security, access control, and surveillance. Achieving better real-time face detection (FD) performance can be challenging, especially when running multiple algorithms that require both high accuracy and swift execution (high frame rate) into embedded System on Chips (SoC). In this study, a comprehensive methodology and system implementation are proposed for concurrent face detection, landmark extraction, quality assessment, and face recognition directly at the edge, without relying on external resources. The approach integrates cutting-edge techniques, including the utilization of the Extended YOLO model for face detection and the ArcFace model for feature extraction, optimized for deployment on embedded devices. By leveraging these models alongside a dedicated recognition database and efficient software architecture, the system achieves remarkable accuracy and real-time processing capabilities. Critical aspects of the methodology involve tailoring model optimization for SoC environments, specifically focusing on the YOLO face detection model and the ArcFace feature extraction model. These optimizations aim to enhance computational efficiency while preserving accuracy. Furthermore, efficient software architecture plays a crucial role, allowing for the seamless integration of multiple components on embedded devices. Optimization techniques are employed to minimize overhead and maximize performance, ensuring real-time processing capabilities. By offering a detailed framework and implementation strategy, this research contributes significantly to the development of a high-performance, highly accurate real-time face recognition system optimized for embedded devices.

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Section: Related Workmentioning

confidence: 99%

Section: Face Detection Subsystemmentioning

confidence: 99%

Real-time face recognition system at the edge

Ozen,

Alim,

Okcu

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…For developing the machine learning model, we inspire from the article by S. B. Okcu et al 10 In this paper, an efficient face quality assessment (FQA) technique by utilizing face quality score calculation with an addition to face landmark detection network is described. With the same logic, we developed a ML model that does not only extract face coordinates, but also creates face quality scores and face landmarks with extra layers added to model.…”

Section: Face Detection Subsystemmentioning

confidence: 99%

Simultaneous license plate recognition and face detection application at the edge

Alim

Kavakli

Okcu

et al. 2023

AI and Optical Data Sciences IV

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Face recognition (FR) and license plate recognition (LPR) are very crucial algorithms for identification of humans and vehicles in several applications such as surveillance, traffic and access-control. The advances in small singleboard computers with high parallel processing power capabilities and the use of low-power Neural Processing Units (NPU) inside embedded System on Chips (SoC), enable real-time face detection (FD) and LPR at the edge. On the other hand, it is still a challenge to run multiple algorithms concurrently with high accuracy and prompt execution (high frame rates) that requires a very efficient software/video analytics algorithm development. Both FR and LPR algorithms need two-stage processing that involve detection and recognition. In this study, we propose a method that enables simultaneous face detection associated with landmark and quality information and LPR at the edge. The FD pipeline detects and tracks the faces, extracts landmarks and quality of faces, to select appropriate faces for recognition and then sends them to face recognition server. LPR algorithm consecutively performs detection and recognition on the embedded platform. Extended YOLO model is utilized for face selection while pruned YOLO and LPRNet models are exploited for license plate detection and license plate reading, respectively. In order to enable real-time performance with high accuracy; optimized AI-models and software architecture are used. As a result of this study, we obtain a high-performance, high-precision and real-time combined face/LPR recognition system which can be very useful for surveillance and security applications.

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“…Some methods incorporate a face quality assessment network into other models that are tailored for various tasks. A method that shares a similar approach is Monet, 27 which integrates face quality extraction into the o-net model, responsible for predicting facial bounding boxes and landmark points. Recently, some methods incorporate face quality assessment to face recognition networks.…”

Section: Related Workmentioning

confidence: 99%

Efficient storage and retrieval of high-quality face images for effective face recognition

Okcu,

Sari,

Çigla

2023

Artificial Intelligence for Security and Defence Applications

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Recent developments in chip technology have led to the integration of smart Artificial Intelligence (AI) capabilities in surveillance cameras such as vehicle and pedestrian detection, license plate recognition, and face detection (FD). FD cameras are being widely deployed in various public locations to identify wanted individuals. However, the large size of identity databases and the need for personal information security make it necessary to perform face recognition (FR) on servers rather than on cameras. On the other hand, more FD cameras and detected faces lead to extensive power consumption and computational power demand for real-time FR along with large storage devices to store face images. While FD cameras track detected faces and select the best quality face for FR, multiple faces belonging to the same identities might be sent to the servers due to track algorithms or reentering of identity to the scene. Therefore, we propose a method for finding similar faces belonging to the same identities and removing low-quality faces to keep only the highest-quality face in the server for storage and FR. We utilize facial embedding vectors, obtained from aligned faces using an FR model, and store them in a database along with face image information such as face capture time, camera IP, and face quality score. If a face has similar faces in the database, the highest-quality face is kept in the database and others are removed. As a result, our proposed method eliminates extra face images and keeps the highest-quality face images for high performance, efficient FR, and effective storage.

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An Efficient Method for Face Quality Assessment on the Edge

Cited by 4 publications

References 37 publications

Real-time face recognition system at the edge

Real-time face recognition system at the edge

Simultaneous license plate recognition and face detection application at the edge

Efficient storage and retrieval of high-quality face images for effective face recognition

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