Deep cross feature adaptive network for facial emotion classification

Reddy, A. Hariprasad; Kolli, Kamakshaiah; Yarrakula, Kiran

doi:10.1007/s11760-021-01941-2

Cited by 14 publications

(4 citation statements)

References 26 publications

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“…Emotion Recognition. Emotions related to e-learning, like boredom, confusion, contempt, curiosity, disgust, eureka, delight, and frustration were mainly identifed in recent literature [32][33][34][35][36][37][38][39]. Deep learning models, mainly convolutional neural networks, are used for emotion classifcation.…”

Section: Machine and Deep Learning Approaches For Facialmentioning

confidence: 99%

“…Diferent deep learning models such as VGGNet [34,39] and ResNet [35] are used for the implementation. A variant of CNN, DCFA-CNN [36], is tested with diferent image datasets and got excellent classifcation result. Yolcu et al [40] presents a deep learning-based system for customer behavior monitoring applications.…”

Section: Machine and Deep Learning Approaches For Facialmentioning

confidence: 99%

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Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

Smitha

Sendhilkumar

Mahalakshmi

2023

Scientific Programming

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Human emotion recognition from videos involves accurately interpreting facial features, including face alignment, occlusion, and shape illumination problems. Dynamic emotion recognition is more important. The situation becomes more challenging with multiple persons and the speedy movement of faces. In this work, the ensemble max rule method is proposed. For obtaining the results of the ensemble method, three primary forms, such as CNNHOG-KLT, CNNHaar-SVM, and CNNPATCH are developed parallel to each other to detect the human emotions from the extracted vital frames from videos. The first method uses HoG and KLT algorithms for face detection and tracking. The second method uses Haar cascade and SVM to detect the face. Template matching is used for face detection in the third method. Convolution neural network (CNN) is used for emotion classification in CNNHOG-KLT and CNNHaar-SVM. To handle occluded images, a patch-based CNN is introduced for emotion recognition in CNNPATCH. Finally, all three methods are ensembles based on the Max rule. The CNNENSEMBLE for emotion classification results in 92.07% recognition accuracy by considering both occluded and nonoccluded facial videos.

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Section: Machine and Deep Learning Approaches For Facialmentioning

confidence: 99%

Section: Machine and Deep Learning Approaches For Facialmentioning

confidence: 99%

Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

Smitha

Sendhilkumar

Mahalakshmi

2023

Scientific Programming

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“…[1,2]. Face recognition aims to give a computer system the ability to quickly and precisely recognize human faces in images or videos [3,4]. Numerous algorithms and methods, including recently proposed deep learning models, have been proposed to improve face recognition performance [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network

et al. 2022

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Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person’s identification. A lack of sufficiently large training datasets is one of the significant challenges that limit the accuracy of face recognition systems. Meanwhile, machine learning (ML) algorithms, particularly those used for image-based face recognition, require large training data samples to achieve a high degree of face recognition accuracy. Based on the above challenge, this research proposes a method for improving face recognition precision and accuracy by employing a hybrid approach of the Gabor filter and a stacked sparse autoencoders (SSAE) deep neural network. The face image datasets from Olivetti Research Laboratory (OLR) and the Extended Yale-B databases were used to evaluate the proposed hybrid model’s performance. All face image datasets used in our experiments are grayscale image type with a resolution of 92 × 112 for the OLR database and a resolution 192 × 168 for the Extended Yale-B database. Our experimental results showed that the proposed method improved face recognition accuracy by approximately 100% for the two databases used at a significantly reduced feature extraction time compared to the current state-of-art face recognition methods for all test cases. The SSAE approach can explore large and complex datasets with minimal computation time. In addition, the algorithm minimizes the false acceptance rate and improves recognition accuracy. This implies that the proposed method is promising and has the potential to enhance the performance of face recognition systems.

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“…Facial expression recognition (FER) has been developed as an important research topic owing to its wide application [1][2][3][4]. Despite deep learning models achieves comparative performance in FER, they mostly leverage vast annotated samples during the training stage.…”

Section: Introductionmentioning

confidence: 99%

Multi-class differentiation feature representation guided joint dictionary learning for facial expression recognition

Sun

Bai

Zhang

2022

Preprint

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The limitation of the small-scale expression samples generally causes the performance degradation for facial expression recognition-based methods. Also, the correlation between different expression is always ignored when performing feature extraction process. Given above, we propose a novel approach that develops multi-class differentiation feature representation guided joint dictionary learning for FER. The proposed approach mainly includes two steps: firstly, we construct multi-class differentiation feature dictionaries corresponding to different expressions of training samples, aiming to enlarge inter-expression distance to mitigate the problem of nonlinear distribution in training samples. Secondly, we joint learn the multiple feature dictionaries by optimizing the resolutions of each feature dictionary, aiming to establish the strong relationship and enhance the representation ability among multiple feature dictionaries. To sum up, the proposed approach has more discriminative ability from the representation perspective. Comprehensive experiments carried out using three public datasets, including JAFFE, CK+, and KDEF datasets, demonstrate that the proposed approach has strong performance for small-scale samples compared to several state-of-the-art methods.

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Deep cross feature adaptive network for facial emotion classification

Cited by 14 publications

References 26 publications

Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network

Multi-class differentiation feature representation guided joint dictionary learning for facial expression recognition

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