Face recognition using scale-adaptive directional and textural features

Mehta, Rakesh; Yuan, Jirui; Егиазарян, Карен

doi:10.1016/j.patcog.2013.11.013

Cited by 47 publications

(26 citation statements)

References 30 publications

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“…(Mehta, Yuan, and Egiazarian 2014) [13]proposes face detection based on extraction of directional and texture feature from the image. The directionality is associated with image is extracted using local polynomial approximation technique (LPA) which is directional filter at multiple scales.…”

Section: Literature Surveymentioning

confidence: 99%

Review of Face Recognition Techniques

Kaur¹,

Kaur²

2017

IJCA

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Face Recognition is used in order to ensure authentication in terms of feature verification. Techniques are defined to identify faces under different situations. This paper conducts a survey of techniques which are available for face detection. Recognition is possible in case features are extracted from the presented face images. For this purpose feature extraction mechanisms like discrete wavelet transformation (DWT), SIFT, linear discriminate analysis (LDA), principal component analysis (PCA) are commonly used. Analysis process indicates that hybrid approach with discrete wavelet transformation produces better results. Comparative study of literature is also presented through this work.

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Section: Literature Surveymentioning

confidence: 99%

Review of Face Recognition Techniques

Kaur¹,

Kaur²

2017

IJCA

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“…Face detection using edge maps and skin color segmentation is presented in [22]. Mehta et al [6] proposed LPA and LBP methods to extract textural features with LDA dimensionality reduction and SVM classification. Lai et al [23] used logarithmic difference edge maps to overcome illumination variation with face verification ability of 95% true positive rate.…”

Section: Related Workmentioning

confidence: 99%

“…Eigenface method is simple and popular method widely used in this field [4]. Other methods like LBP [5], LDA [6], Gabor Filter [7], etc. Neural Network is efficient method used for classification [8].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition and Tracking in Videos

Tathe¹,

Narote²,

Narote³

2017

Adv. sci. technol. eng. syst. j.

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“…Automatic landmark detector estimates pose and detect missed data within the partial face model. Rakesh [22] utilize directional and texture information from face images for face recognition. The scale adaptive digital filters and local descriptors were used to capture directionality and to extract features respectively.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

Ramalingam¹,

Dhanushkodi²

2016

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The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.

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Face recognition using scale-adaptive directional and textural features

Cited by 47 publications

References 30 publications

Review of Face Recognition Techniques

Review of Face Recognition Techniques

Face Recognition and Tracking in Videos

Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

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