Estimating Attention of Faces Due to its Growing Level of Emotions

Kumar, Rajneesh; Garain, Jogendra; Kisku, Dakshina Ranjan; Sanyal, Goutam

doi:10.1109/cvprw.2018.00261

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Kumar et al discussed the modeling of abnormal facial expressions based on computer vision tasks and emotional deviations. They found that deep CNN could play an essential role in the training and classification of facial expressions, which provided a new visual modeling method for visual surveillance systems [ 15 ]. Mishra et al employed CNN to recognize different emotions and intensity levels of human faces, which provided the basis and support for future research on computer emotion recognition [ 16 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

Zhu

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the application of deep learning to psychological feature extraction. First, the relationship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is constructed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face images and the optimization of relevant parameters. Compared with other methods, the CNN-CLBP algorithm has higher accuracy in facial expression recognition, with an average recognition rate of 88.16%. Besides, the recognition accuracy of this algorithm is improved by image preprocessing and parameter optimization, and there is no poor-fitting. Moreover, the CNN-CLBP algorithm can recognize 97% of the happy expressions and surprised expressions, but the misidentification rate of sad expressions is 22.54%. The research result provides data reference and direction for analyzing psychological characteristics of juveniles involved in crimes.

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Section: Literature Reviewmentioning

confidence: 99%

Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

Zhu

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…As various factors about human faces have been proved to persuade the visual attention, researchers initiate to combine face cues into saliency modeling. In the literatures (Min et al 2017a;Kant Kumar et al 2018), authors have built an attention model mainly for the face images. They combined the lowlevel features (Computed by some existing saliency models) with high-level facial features.…”

Section: Facial Cue Based Saliency Modelsmentioning

confidence: 99%

Guiding attention of faces through graph based visual saliency (GBVS)

et al. 2019

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In a general scenario, while attending a scene containing multiple faces or looking towards a group photograph, our attention does not go equal towards all the faces. It means, we are naturally biased towards some faces. This biasness happens due to availability of dominant perceptual features in those faces. In visual saliency terminology it can be called as 'salient face'. Human's focus their gaze towards a face which carries the 'dominating look' in the crowd. This happens due to comparative saliency of the faces. Saliency of a face is determined by its feature dissimilarity with the surrounding faces. In this context there is a big role of human psychology and its cognitive science too. Therefore, enormous researches have been carried out towards modeling the computer vision system like human's vision. This paper proposed a graphical based bottom up approach to point up the salient face in the crowd or in an image having multiple faces. In this novel method, visual saliencies of faces have been calculated based on the intensity values, facial areas and their relative spatial distances. Experiment has been conducted on gray scale images. In order to verify this experiment, three level of validation has been done. In the first level, our results have been verified with the prepared ground truth. In the second level, intensity scores of proposed saliency maps have been cross verified with the saliency score. In the third level, saliency map is validated with some standard parameters. The results are found to be interesting and in some aspects saliency predictions are like human vision system. The evaluation made with the proposed approach shows moderately boost up results and hence, this idea can be useful in the future modeling of intelligent vision (robot vision) system.

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DeepFusion-Net: A U-Net and CGAN-Based Approach for Salient Object Detection

Dhara,

Kumar

2024

Proceedings of 4th International Conference on Frontiers in Computing and Systems

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Estimating Attention of Faces Due to its Growing Level of Emotions

Cited by 5 publications

References 19 publications

Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

Guiding attention of faces through graph based visual saliency (GBVS)

DeepFusion-Net: A U-Net and CGAN-Based Approach for Salient Object Detection

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