A face detection method based on kernel probability map

Mahmoodi, Mohammad Reza; Sayedi, Sayed Masoud

doi:10.1016/j.compeleceng.2015.02.005

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…To validate the proposed face recognition method, taking into account of the above report, we have constructed a face image database on our interest, and its details are given below. Also, we have considered the datasets, such as GT dataset (Mahmoodi and Sayedi 2015 ; Muqeet and Holambe 2019 ), LFW dataset (Mahmoodi and Sayedi 2015 ), Pointing’04 dataset (Dantcheva et al 2018 ), BioID dataset (BioID) for the experiments.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The F1 score obtained by the ARBBPNN method and the F1 scores available in Mahmoodi and Sayedi ( 2015 ), Muqeet and Holambe ( 2019 ) and Liu et al ( 2019 ) have been taken into account for comparison, which is given in Table 3 . In Mahmoodi and Sayedi ( 2015 ), the F1 score is available only for the GT and LFW datasets; in CRRF (Liu et al 2019 ) method, the F1 score was calculated for LFW dataset; we calculated the F1 score for the DIWT method using the rank-one score and other results available in Muqeet and Holambe ( 2019 ). The results presented in Table 3 show that the proposed method gives better results than the state-of-the-art methods in terms of F1 score.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

Vasanthi

Seetharaman

2021

Soft Comput

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This paper proposes a novel method, which is coined as ARBBPNN, for biometric-oriented face detection, based on autoregressive model with Bayes backpropagation neural network (BBPNN). Firstly, the given input colour key face image is modelled to HSV and YCbCr models. A hybrid model, called HS-YCbCr, is formulated based on the HSV and YCbCr models. The submodel, H, is divided into various sliding windows of size, 3 9 3. The model parameters are estimated for the window using the BBPNN. Based on the model coefficients, autocorrelation coefficients (ACCs) are computed. An autocorrelation test tests the significance of the ACCs. If the ACC passes the test, then it is inferred that the small image region, viz. the window, represents the texture and it is treated as the texture feature. Otherwise, it is regarded as structure, which is treated as the shape feature. The texture and shape features are formulated as feature vectors (FV) separately, and they are combined into a single FV. This process is performed for all colour submodels. The FVs of the submodels are combined into a single holistic vector, which is treated as the FV of the key face image. The key FV has twenty feature elements. The similarity of the key and target face images is examined, based on the key and target FVs, by deploying multivariate parametric statistical tests. If the FVs of the key and target images pass the tests, then it is concluded that the key and target face images are the same; otherwise, they are regarded as different. The GT, FSW, Pointing'04, and BioID datasets are considered for the experiments. In addition to the above datasets, we have constructed a dataset with face images collected from Google, and many images captured through a digital camera. It is also subjected to the experiment. The obtained recognition results show that the proposed ARBBPNN method outperforms the existing methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

Vasanthi

Seetharaman

2021

Soft Comput

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“…In order to corroborate the validity of the system, two image processing algorithms namely a skin detection and a motion detection is designed. For the former, the aim is to group all the pixels into two classes of skin and non-skin pixels for any image [26,27]. This has numerous applications in surveillance, content based coding, and face detection [28,29,30], etc.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

A Low Cost High Speed FPGA-Based Image Processing Framework

Mahmoodi

Sayedi

2016

IJMECS

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Abstract-In this paper, a high-speed and low-cost image processing framework based on MATLAB-FPGA interface is proposed that can be used in researches aiming at developing wide variety of not only image processing tasks but also many signal processing applications. In addition, this new framework could be exploited for several other tasks such as on-chip verification, using PC as an enormous external RAM for FPGA while preserving high speed data access, developing hardware-software co-designs, etc. The communication between FPGA and MATLAB is via 1Gbs Ethernet based on UDP/IP protocol which is very promising for high speed data transmission in point-topoint communications. UDP stack is efficiently designed in FPGA based on a fully pipelined architecture with minimum level of logic in order to reach high performance.. Dynamic data transmission between the UDP stack, memory and an arbitrary image processing module makes it possible to practically simulate, debug and implement most relevant applications. The hardware system is relatively low-cost and it consumes a negligible area of a Spartan-6 LXT45 Xilinx FPGA. Operating at 1 Gb/s, theoretically, the system is capable of processing 132 frames of 640*480 color images in a second. The effectiveness of the system is evaluated by means of both place and route simulation and practical implementation of a skin detection algorithm and a motion detector.

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“…They segmented the skin cluster in YC b C r color model into 3 non-overlapping regions; pixels with high probability to be skin, non-skin pixels and pixels with unknown status. They used the first category of pixels to generate initial result and then subsequently used a neighboring procedure for each skin pixel to include more skin pixels and finally a window based procedure is used to determine qualified windows for their further face detection [190,191]. In [142], after obtaining skin probability of the image, hysteresis thresholding is employed to segment skin blobs.…”

Section: Spatial Based Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

Self Cite

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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A face detection method based on kernel probability map

Cited by 13 publications

References 30 publications

A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

A hybrid method for biometric authentication-oriented face detection using autoregressive model with Bayes Backpropagation Neural Network

A Low Cost High Speed FPGA-Based Image Processing Framework

A Comprehensive Survey on Human Skin Detection

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