RETRACTED CHAPTER: Real-Life Facial Expression Recognition Systems: A Review

Goyal, Samta Jain; Upadhyay, Arvind Kumar; Jadon, R. S.; Goyal, Rajeev

doi:10.1007/978-981-10-5544-7_31

Cited by 14 publications

(3 citation statements)

References 78 publications

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“…It includes images and videos captured from both front and side views. It includes data from 75 adults showing different basic emotions and their more than 2900 images or videos related to expression responses [23]. This dataset addresses some key omissions of the usual expression datasets, particularly it contains the whole process of a full timeline change pattern of facial expression.…”

Section: Datasetmentioning

confidence: 99%

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Chen

Shen

Fan

2023

Preprint

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To solve the problem of trait differences in expression characteristics among different age groups, shortage of research on cross-age facial expression analysis, and the lack of datasets across ages, put forward the model in age factor deep sparse fusion extension of the expression recognition. The model utilizes a linear dictionary sequence of a high-level form as an input signal for a sparse representation, the input objects are constructed in a linear combination, and select the optimal solution in all solutions with the sparse level as an indicator, further through the convolutional, pooling and full connection processing of convolutional neural networks, the problems of high similarity, insufficient quantity and uneven distribution of data sets are solved through the fusion extension strategy, on this basis, the age operator is combined as feature elements and expression features are extracted and used as the basis for classification decision. Extension by deep sparse fusion, additional factors were extracted using anthropometric models, internal angle calculation and skin wrinkle detection of age operator as expression features, the comparison of results of multiple dataset experiments demonstrate the effectiveness and stability across datasets of the algorithm, the advantages of this algorithm are also demonstrated by the horizontal alignment of the representative cross-age expression classification algorithm. This method is effective for the accuracy and robustness of expression expressions across ages, it has the research value and the reference significance.

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Section: Datasetmentioning

confidence: 99%

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Chen

Shen

Fan

2023

Preprint

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“…The blockage of the face picture can be with glasses, shades, and scarf as appeared in Figure 4 (Oloyede et al, 2020). Expression Variation: Human beings have the ability to display various facial expressions unless if the face image is in a static mode (Goyal et al, 2017). These expressions are used to represent different emotions and mental states of a particular person as shown in Figure 5 (Zhou & Xiao, 2018).…”

Section: Figure 1 Face Recognition Challengesmentioning

confidence: 99%

A State-of-the-Art Survey on Face Recognition Methods

Modi¹,

Patel²

2021

International Journal of Computer Vision and Image Processing

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Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

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“…This communication is so deep and rooted that it is difficult to hide and deceive. He knows what is happening in his environment [1]. These reactions are clearly shown in the voice, the way the body moves, and the face.…”

Section: Introductionmentioning

confidence: 98%

Improving recognition accuracy for facial expressions using scattering wavelet

Davari,

Harooni,

Nasr

et al. 2024

EAI Endorsed Trans AI Robotics

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One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.

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RETRACTED CHAPTER: Real-Life Facial Expression Recognition Systems: A Review

Cited by 14 publications

References 78 publications

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

Age Operator Deep Sparse Fusion Extension of the Expression Recognition

A State-of-the-Art Survey on Face Recognition Methods

Improving recognition accuracy for facial expressions using scattering wavelet

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