A robust face recognition algorithm under varying illumination using adaptive retina modeling

Cheong, Yuen Kiat; Yap, Vooi Voon; Nisar, Humaira

doi:10.1063/1.4825007

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In addition to the above algorithms, other techniques were also successfully suggested for face detection and face recognition. This includes Gabor Filter [66], Gaussian Face, Eigen Distance [67], Linear Discriminant Analysis (LDA), and Kernel Fisher Analysis [68]. For instance, in [69] a face recognition system using a subset of the orthogonal Gabor filter for feature extraction was proposed.…”

Section: ) Other Non-ai Techniques For Face Detection and Recognitionmentioning

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: ) Other Non-ai Techniques For Face Detection and Recognitionmentioning

confidence: 99%

Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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“…There are numerous techniques available for face detection using thermal images and normal images (knowledge based methods, feature invariant facial approaches, template matching methods and appearance based methods [4,10]). One of the famous methods for face detection, is proposed by Viola and Jones [5].…”

Section: Introductionmentioning

confidence: 99%

A novel face detection algorithm using thermal imaging

Cheong

Yap

Nisar

2014

2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

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Face detection is an important step for successful face recognition system. Variation in lighting conditions result in degradation in performance of face detection. Thermal images are not affected by variation in illumination. In this paper, we have proposed a novel face detection algorithm by using thermal imaging. In our proposed algorithm, Otsu's thresholding method is used for converting the thermal images into binary form. Horizontal projection of the image is calculated to identify the global minimum that helps in identifying height and width of the head region. We have developed a UTAR-YK Thermal database to verify the performance of the proposed algorithm. Experimental results show that our proposed algorithm has an average accuracy of 92.16%, that is measured by finding the overlapping ratio between the automatically-detected face region and manually drawn face region. An average 1.13 bounding box ratio, which is the comparison of the size of the box between the automatically-detected face region and manually drawn face region.

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A robust face recognition algorithm under varying illumination using adaptive retina modeling

Cited by 2 publications

References 13 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

A novel face detection algorithm using thermal imaging

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