A face emotion tree structure representation with probabilistic recursive neural network modeling

Wong, Jia-Jun; Cho, Siu-Yeung

doi:10.1007/s00521-008-0225-z

Cited by 22 publications

(8 citation statements)

References 45 publications

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“…When six emotions are used, the CRR of the proposed approach is 9.73% and 15.88% higher than those in [42] and [43] respectively on the JAFFE database, as well as 1.88% higher than that obtained using LBP features in [26] on the CK database. The result of the proposed approach on the CK database is 0.62% lower than the result obtained using the boosted-LBP features in [26] and 1.39% lower than the result in [46]. However, the work [26] normalized the face based on manually labeled eye locations and improved the results by optimizing the SVM parameters.…”

Section: Perforamnce Under Registration Errorsmentioning

confidence: 88%

“…The recognition results in [41] were obtained by removing two JAFFE images named "KR.SR3.79" and "NA.SU1.79". As shown in table 7, the proposed approach outperforms all nine benchmarked approaches ( [23], [26], [40], [41], [42], [43], [44], [45], [46]) when the JAFFE database is used, and three of four benchmarked approaches ( [20], [21], [26], [46]) when the CK database is used. When six emotions are used, the CRR of the proposed approach is 9.73% and 15.88% higher than those in [42] and [43] respectively on the JAFFE database, as well as 1.88% higher than that obtained using LBP features in [26] on the CK database.…”

Section: Perforamnce Under Registration Errorsmentioning

confidence: 98%

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Facial Expression Recognition Using Facial Movement Features

Zhang

Tjondronegoro

2011

IEEE Trans. Affective Comput.

213

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Abstract-Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using 'salient' distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the 'salient' patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

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Section: Perforamnce Under Registration Errorsmentioning

confidence: 88%

Section: Perforamnce Under Registration Errorsmentioning

confidence: 98%

Facial Expression Recognition Using Facial Movement Features

Zhang

Tjondronegoro

2011

IEEE Trans. Affective Comput.

213

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show abstract

“…However, the approach in [2] normalizes facial images based on manually-labeled eye locations, while the proposed approach is only based on rough face location. The result using CK is 1.39% lower than the result in [8]. But the approach in [8] obtains the result based on 5-fold cross validation and 5 emotions, therefore, it uses more training images to classify less emotions compared to our approach.…”

Section: Compar Ison With State-of-the-ar T Per For Mancementioning

confidence: 76%

“…The result using CK is 1.39% lower than the result in [8]. But the approach in [8] obtains the result based on 5-fold cross validation and 5 emotions, therefore, it uses more training images to classify less emotions compared to our approach. …”

Section: Compar Ison With State-of-the-ar T Per For Mancementioning

confidence: 76%

Improving the Performance of Facial Expression Recognition Using Dynamic, Subtle and Regional Features

Zhang

Tjondronegoro

2010

Lecture Notes in Computer Science

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Abstr act. Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based 'salient' Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a toplevel performance on the Cohn-Kanade (CK) database.

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“…To achieve sufficient correspondence when the probe and gallery images are not in the same pose and expression, we use face components instead of holistic features. Four typical key points on faces are manually marked to guide the partition [58], i.e., center of two eyes, nose tip, and mouth center, as shown in Fig. 6.…”

Section: B Results On Buaa-visnir Face Database 1) Database Descriptmentioning

confidence: 99%

Cross-Modality Feature Learning Through Generic Hierarchical Hyperlingual-Words

Shao

2017

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-Recognizing facial images captured under visible light has long been discussed in the past decades. However, there are many impact factors that hinder its successful application in real-world, e.g., illumination, pose variations. Recent work has concentrated on different spectrals, i.e., near infrared, that can only be perceived by specifically designed device to avoid the illumination problem. However, this inevitably introduces a new problem, namely, cross-modality classification. In brief, images registered in the system are in one modality, while images that captured momentarily used as the tests are in another modality. In addition, there could be many within-modality variationspose and expression-leading to a more complicated problem for the researchers. To address this problem, we propose a novel framework called hierarchical hyperlingual-words (Hwords) in this paper. First, we design a novel structure, called generic Hwords, to capture the high-level semantics across different modalities and within each modality in weakly supervised fashion, meaning only modality pair and variations information are needed in the training. Second, to improve the discriminative power of Hwords, we propose a novel distance metric through the hierarchical structure of Hwords. Extensive experiments on multimodality face databases demonstrate the superiority of our method compared with the state-of-the-art works on face recognition tasks subject to pose and expression variations.

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A face emotion tree structure representation with probabilistic recursive neural network modeling

Abstract: This paper describes a novel structural approach to recognize the human facial features for emotion recognition. Conventionally, features extracted from facial images are represented by relatively poor representations, such as arrays or sequences, with a static data structure.

Cited by 22 publications

References 45 publications

Facial Expression Recognition Using Facial Movement Features

Facial Expression Recognition Using Facial Movement Features

Improving the Performance of Facial Expression Recognition Using Dynamic, Subtle and Regional Features

Cross-Modality Feature Learning Through Generic Hierarchical Hyperlingual-Words

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