Face recognition system based on a single-board computer

Lindner, Tymoteusz; Wyrwał, Daniel; Białek, Marcin; Nowak, Patryk

doi:10.1109/msm49833.2020.9201668

Cited by 15 publications

(15 citation statements)

References 12 publications

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“…where five facial landmarks are generated [83]. MTCNN which has been successfully used for face detection in ( [2], [84], [85], [86]), features an advantage over FaceNet is that it can simultaneously detect more than one face in an image and feed them to a recognition system. On the other hand, FaceNet can detect only one face at a time [2].…”

Section: ) Mtcnnmentioning

confidence: 99%

“…Nevertheless, several researchers revealed its superior accuracy over Viola-Jones [88]. In [85] a face detection and recognition system was implemented on various hardware platforms that include multicore CPU Raspberry PI-3+ and NVIDIA's Jetson nano GPU. Two face detection algorithms: Haar feature based and MTCNN were tested and compared.…”

Section: ) Mtcnnmentioning

confidence: 99%

“…FaceNet is a face recognition system developed by Google [85]. The system as described in details in [91], extracts highquality features from the face and predicts a 128-elements vector representation called a face embedding.…”

Section: ) Facenetmentioning

confidence: 99%

“…The system as described in details in [91], extracts highquality features from the face and predicts a 128-elements vector representation called a face embedding. Face embeddings are then mapped to generate a compact Euclidean space, where L2 distances are calculated to measure face similarity [85] [91]. Face recognition becomes then a K-NN classification problem, while face clustering is either kmeans or agglomerative clustering.…”

Section: ) Facenetmentioning

confidence: 99%

“…The system latency was 57 ms, which was much slower than the cloud Microsoft azure which yielded 893 ms latency. Similarly, in [85] a cascaded detection and recognition network was developed. For face detection, two different algorithms; Haar feature-based and MTCNN were tested, and FaceNet was used for recognition.…”

Section: ) Facenetmentioning

confidence: 99%

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Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

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