Facial expression recognition based on salient patch selection

Zhang, Rui; Li, Jie; Xiang, Zhenzhen; Su, Jianbo

doi:10.1109/icmlc.2016.7872938

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Zhang et al. 13 proposed a novel facial expression recognition method by finding details of each expression. For this replace the complete set of feature expressions with a whole facial feature that improves accuracy and saves time.…”

Section: Related Workmentioning

confidence: 99%

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Sonal¹,

Singh²,

Kant

2021

International Journal of Electrical Engineering & Education

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Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

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Section: Related Workmentioning

confidence: 99%

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Sonal¹,

Singh²,

Kant

2021

International Journal of Electrical Engineering & Education

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“…However, their algorithm did not achieve high recognition rates in experiments. Zhang et al [14] used a sparse group lasso scheme to explore the most salient patches for each facial expression, and they combined these patches into the final features for emotion recognition. They achieved an average recognition rate of 95.33% on the CK+ database.…”

Section: Related Workmentioning

confidence: 99%

“…An effective feature extraction method is thus necessary for recognizing non-frontal facial expressions. Recently, a method based on salient facial patches, which seeks salient facial patches from the human face and extracts facial expression features from these patches, has played a significant role in emotion recognition [11][12][13][14][15][16][17][18][19]. In this method, select facial patches (e.g., eyebrows, eyes, cheeks, and mouth) are considered the key regions of face images, and the discriminative features are extracted from salient regions.…”

Section: Introductionmentioning

confidence: 99%

Non-frontal facial expression recognition based on salient facial patches

Jiang

Zhang

et al. 2021

J Image Video Proc.

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Methods using salient facial patches (SFPs) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and they do not consider head position variations. We contend that SFP can be an effective approach for recognizing facial expressions under different head rotations. Accordingly, we propose an algorithm, called profile salient facial patches (PSFP), to achieve this objective. First, to detect facial landmarks and estimate head poses from profile face images, a tree-structured part model is used for pose-free landmark localization. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks while avoiding their overlap or the transcending of the actual face range. To analyze the PSFP recognition performance, three classical approaches for local feature extraction, specifically the histogram of oriented gradients (HOG), local binary pattern, and Gabor, were applied to extract profile facial expression features. Experimental results on the Radboud Faces Database show that PSFP with HOG features can achieve higher accuracies under most head rotations.

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“…However, their algorithm did not achieve high recognition rates in their experiment. Zhang et al 14 used a sparse group lasso scheme to explore the most salient patches for each facial expression and combined these patches into the final features for emotion recognition. They achieved an average recognition rate of 95.33% on the CK+ database.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, a method based on salient facial patches, which seeks salient facial patches from the human face and extracts facial expression features from these patches, has played a significant role in emotion recognition [11][12][13][14][15][16][17][18][19] . In this method, a few prominent facial patches (e.g., eyebrows, eyes, cheeks, and mouth) are relied on as the key points in face images, and the discriminative features are extracted from salient regions.…”

Section: Introductionmentioning

confidence: 99%

Non-frontal Facial Expression Recognition Based on Salient Facial Patches

Jiang

Zhang

et al. 2020

Preprint

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Methods using salient facial patches (SFP) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and do not consider variations of head position. In our view, SFP can also be a good choice to recognize facial expression under different head rotations, and thus we propose an algorithm for this purpose, called Profile Salient Facial Patches (PSFP). First, in order to detect the facial landmarks from profile face images, the tree-structured part model is used for pose-free landmark localization; this approach excels at detecting facial landmarks and estimating head poses. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks, while avoiding overlap with each other or going beyond the range of the actual face. For the purpose of analyzing the recognition performance of PSFP, three classical approaches for local feature extraction-histogram of oriented Gradients (HOG), local binary pattern (LBP), and Gabor were applied to extract profile facial expression features. Experimental results on radboud faces database show that PSFP with HOG features can achieve higher accuracies under the most head rotations.

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Facial expression recognition based on salient patch selection

Cited by 5 publications

References 16 publications

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Non-frontal facial expression recognition based on salient facial patches

Non-frontal Facial Expression Recognition Based on Salient Facial Patches

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