Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

Hasan, Khaled; Ahsan, Md. Shamim; Abdullah-Al-Mamun,; Newaz, S. H. Shah; Lee, Gyu Myoung

doi:10.3390/electronics10192354

Cited by 53 publications

(29 citation statements)

References 242 publications

(323 reference statements)

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“…The paper concluded that feature-based face recognition algorithms achieved the highest recognition accuracy compared to appearance-based and soft computing-based algorithms, with a normalized rate of 95%. In a very recent review paper [8], a wide variety of face detection algorithms are divided into feature-based and image-based approaches. The authors provided a detailed description along with a comparative evaluation of all algorithms among the two categories.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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“…It was known for its capability of processing images extremely rapidly while achieving high detection rates [3]. Face detector is essential to almost all recent important applications based on face analysis, such as face image enhancement [4] , face recognition [5] [6], face identification [7], face verification, face alignment [8], face feature extraction [9], face emotion recognition, and gender classification. One of the popular applications used today in mobile devices is the snapchat (snapchat application apply filters to face but first it has to find the face, and this is obtained by using Viola-Jones).…”

Section: Introductionmentioning

confidence: 99%

evaluation study of face detection by Viola-Jones algorithm

Abdulhussien

Saud

2022

ijhs

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Face detection technology is an essential step in almost all face related analysis applications such as face feature extraction, face alignment, face verification, face identification, face parsing, face recognition, age recognition, and gender classification. Numerous algorithms were introduced for face detection, one of which is the Viola-Jones algorithm being introduced in 2001. This algorithm is still widely used due to its simplicity and ability of detection in real-time with relatively high accuracy and low computational power requirements compared to other recent algorithms such as deep learning based algorithms. In this paper, Viola-Jones algorithm is implemented and evaluated through different tests. And its strengths, limitations, and affecting factors are provided according to the obtained results. This paper concentrates on the algorithm limitations and the reasons of these limitations, and suggests some solutions if possible. This can help in enhancing the algorithm performance by increasing the detection accuracy or reducing the time taken for detection or training…etc.

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“…Advances in information and communication technologies over the past years have expanded the scope of application of human skin detection: face detection [1][2][3], visual tracking for surveillance [4], gesture recognition [5], image retrieval and filtering image contents on the web [6], face recognition system [7,8], to mention just a few examples.…”

Section: Introductionmentioning

confidence: 99%

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

Soltani¹,

Benabdelkader²

2021

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The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

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Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

Cited by 53 publications

References 242 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

evaluation study of face detection by Viola-Jones algorithm

Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

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