Local feature extraction based facial emotion recognition: a survey

Slimani, K.; Kas, Mohamed; Merabet, Youssef El; Ruichek, Yassine; Messoussi, Rochdi

doi:10.11591/ijece.v10i4.pp4080-4092

Cited by 10 publications

(8 citation statements)

References 17 publications

(20 reference statements)

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“…Artificial intelligence has enabled biometric recognition and the efficient unpacking of human emotions and affective and physiological responses and has contributed considerably to advances in the field of pattern recognition in biometrics, emotions, and affective attitudes. Many different AI algorithms are used in the world, such as machine learning, artificial neural networks [ 535 , 536 , 537 ], search algorithms [ 166 , 538 , 539 ], expert systems [ 540 , 541 ], evolutionary computing [ 542 , 543 ], natural language processing [ 544 , 545 ], metaheuristics, fuzzy logic [ 546 , 547 , 548 ], genetic algorithm [ 549 , 550 , 551 ], and others.…”

Section: Resultsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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Section: Resultsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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“…The integrated HBD is stored in the temporary database and metadata of the cloud-wide HBD resource layer and then stored in the cloud-wide HBD integration layer, which is applied to practical application software to complete the integration of cloud-wide HBD based on integrated learning so as to ensure that the cloud-wide HBD resources have higher value embodiment and manageability when integrated [ 15 , 16 ].…”

Section: Methodsmentioning

confidence: 99%

Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

Chen

2022

Journal of Healthcare Engineering

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In chorus activities, the conductor leads chorus members to recreate music works. If you want to interpret music works perfectly with sound, emotion and emotional expression are particularly important. In this paper, a cloud HBD (health big data) integration system based on ensemble learning is designed to realize the high-efficiency and high-precision integration of HBD. An emotional speech database containing three emotions such as pleasure, calmness, and boredom is established, and the corpus problems such as emotional feature analysis and extraction needed for chorus emotion recognition research are solved. It also studies the classification and decision-making in emotional changes, and a DBN (deep belief network) chorus emotion recognition algorithm based on multiple emotional features is proposed. Feature DBN (Deep Belief Network) Chorus Emotion Recognition Algorithm This paper extracts various robust low-level features according to different features' ability to describe emotions and then feeds them into the DBN network to extract high-level feature descriptors. Then, the classification results of ELM (extreme learning machine) are voted and fused with the idea of ensemble learning, and the effectiveness of the algorithm is proved on three public datasets.

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“…Principal component analysis (PCA) and LDA were used to reduce the feature dimensions, and cosine similarity assessment was used for identification. Slimani et al [7] aimed to bridge the gap by conducting a large-scale evaluation of 46 variables using LBPs to identify facial expressions. Those authors conducted their experiments on various datasets (candidate key (CK), the Japanese female facial expression (JAFFE), and multimedia understanding group (MUG) facial expression database) to get results of 100%, 95%, and 96% respectively.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Ogla

Saeid

Shaker

2022

IJECE

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<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

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Local feature extraction based facial emotion recognition: a survey

Cited by 10 publications

References 17 publications

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Research on Chorus Emotion Recognition and Intelligent Medical Application Based on Health Big Data

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

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