Deep features-based expression-invariant tied factor analysis for emotion recognition

Munasinghe, Sarasi; Fookes, Clinton; Sridharan, Sridha

doi:10.1109/btas.2017.8272741

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…To fairly compare with other methods, we do not implement any pre-processing algorithm on it. But IcRL also shows satisfying performance (72.36% recognition accuracy), surpassing the baseline model by a large margin and beating several competitive methods, including AlexNet [41], CNN-Ensemble [42], RTCNN [43], DNN [13], DFEITF [44] and LSTM [45]. Surprisingly, the attention module here does not bring more improvement as it does on other databases, but actually causes about 1% drop.…”

Section: ) Experiments On Baum-2i and Fer2013mentioning

confidence: 90%

Facial Expression Recognition by Inter-Class Relational Learning

Chen

2019

IEEE Access

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Certain correlation exists between various expressions of the human face, yet few works have explored it. In this paper, we propose to recognize facial expressions by extracting independent expressive representation through a novel learning procedure, called Inter-class Relational Learning (IcRL). First, an elaborately designed CNN architecture is utilized for simultaneously extracting features from two different expression images. Then, two extracted features are integrated with a random ratio so that a hybrid feature can be attained. An attention module is proposed to assign a weight for each pixel of the hybrid feature. At last, we feed the weighted hybrid feature to the subsequent classification module. The goal of the whole network training is to output the correct ratio of each expression. Different from previous work, our novel method is capable of learning the mutual relations between different classes of expression and enlarging Fisher's criterion between any two classes, i.e., the ratio of inter-class distance to intra-class distance, by accurately discriminating the hybrid feature. In this way, we will be able to enhance the discriminative power of learned features of each expression and, therefore, can improve the classification performance. The IcRL method has been evaluated on five public expression databases: CK+, JAFFE, TFEID, BAUM-2i, and FER2013. The experimental results demonstrate the superiority of the proposed method over many state-of-the-art approaches.

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Section: ) Experiments On Baum-2i and Fer2013mentioning

confidence: 90%

Facial Expression Recognition by Inter-Class Relational Learning

Chen

2019

IEEE Access

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“…The method proposed by Arriaga et al [26] obtains an accuracy of 70.74%. Munasinghe et al [50] propose a sequential-based framework which achieves a recognition accuracy of 71.10%. Xie et al [51] propose a deep multi-path CNN with salient region attention and obtain an average accuracy of 72.5%.…”

Section: ) Comparison On Fer2013mentioning

confidence: 99%

Facial Expression Recognition Using Hybrid Features of Pixel and Geometry

Liu

Hirota

et al. 2021

IEEE Access

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Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This article presents a hybrid feature extraction network to enhance the discriminative power of emotional features. The proposed network consists of a Spatial Attention Convolutional Neural Network (SACNN) and a series of Long Short-term Memory networks with Attention mechanism (ALSTMs). The SACNN is employed to extract the expressional features from static face images and the ALSTMs is designed to explore the potentials of facial landmarks for expression recognition. A deep geometric feature descriptor is proposed to characterize the relative geometric position correlation of facial landmarks. The landmarks are divided into seven groups to extract deep geometric features, and the attention module in ALSTMs can adaptively estimate the importance of different landmark regions. By jointly combining SACNN and ALSTMs, the hybrid features are obtained for expression recognition. Experiments conducted on three public databases, FER2013, CK+, and JAFFE, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 74.31%, 95.15%, and 98.57%, respectively. The preliminary results of Emotion Understanding Robot System (EURS) indicate that the proposed method has the potential to improve the performance of human-robot interaction.

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Facial Expression Recognition Based on Double-Channel Convolutional Neural Network

Yan

Guo

2022

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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Deep features-based expression-invariant tied factor analysis for emotion recognition

Cited by 7 publications

References 22 publications

Facial Expression Recognition by Inter-Class Relational Learning

Facial Expression Recognition by Inter-Class Relational Learning

Facial Expression Recognition Using Hybrid Features of Pixel and Geometry

Facial Expression Recognition Based on Double-Channel Convolutional Neural Network

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