Facial expression recognition based on two-dimensional discriminant locality preserving projections

Zhi, Ruicong; Ruan, Qiuqi

doi:10.1016/j.neucom.2007.12.002

Cited by 69 publications

(26 citation statements)

References 6 publications

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“…However, such a database is less complex [9] 90.1 Geometry and Gabor [11] 92.5 Gabor filters [13] 92.3 Gabor filters [15] 94.5 LBP, Tsallis Entrop., Global App. [16] 95.9 2D Locality Preserving Projec. [17] 90.2 Gabor and LVQ Proposed Appr.…”

Section: Resultsmentioning

confidence: 99%

Facial expression recognition using ensemble of classifiers

Zavaschi

Koerich

Oliveira

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a novel method for facial expression classification that employs the combination of two different feature sets in an ensemble approach. A pool of base classifiers is created using two feature sets: Gabor filters and local binary patterns (LBP). Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the accuracy and the size of the ensemble. The experimental results on two databases have shown the efficiency of the proposed strategy by finding powerful ensembles, which improves the recognition rates between 5% and 10%.

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Section: Resultsmentioning

confidence: 99%

Facial expression recognition using ensemble of classifiers

Zavaschi

Koerich

Oliveira

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Bai et al [4] Gabor+LBP+LDA 92% to 97% Zhi and Ruan [30] 2D discriminant LPP 95.91% Zhang et.al. [38] Multilayer Perceptron 90.34% Liejun et al [32] SVM based 95.7% Zhao et al [34] PCA and NMF 93.72% Lee [33] RDAB 96.67%…”

Section: A Database Usedmentioning

confidence: 99%

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Hegde¹,

Seetha²

2017

IJIGSP

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Abstract-This paper demonstrates mainly on enhancement of extracted feature and proposes a novel approach for feature level fusion for efficient expression recognition. Extracted Gabor filter magnitude feature vector has been fused with upper face part geometrical features and Gabor phase feature vector has been fused with lower face part geometrical features respectively. Both these high dimensional feature dataset have been projected into low dimensional subspace for decorrelating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset yields higher accuracy rates compared to state of art approaches.

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“…Bartlett et al [2006], Wang and Guan [2008], Zhi and Ruan [2008] used Gabor wavelets based features for emotion detection. Beside these, which are mostly used, Zhi and Ruan [2008] report the usage of other facial features in related work: active appearance models (AAM), action units, various facial points and motion units, Haar based features and textures. Various classification schemes were used successfully in video emotion detection.…”

Section: Related Workmentioning

confidence: 99%

“…Bartlett et al [2006] employed both the Support Vector Machine (SVM) and AdaBoost classifiers. Zhi and Ruan [2008] used the knearest neighbours (k-NN) algorithm. Before using the classifier they performed a dimensionality reduction step using the locality preserving projection (LPP) technique.…”

Section: Related Workmentioning

confidence: 99%

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

Tkalčič

Odi

et al. 2012

Lecture Notes in Computer Science

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Abstract. Affective labeling of multimedia content can be useful in recommender systems. In this paper we compare the effect of implicit and explicit affective labeling in an image recommender system. The implicit affective labeling method is based on an emotion detection technique that takes as input the video sequences of the users' facial expressions. It extracts Gabor low level features from the video frames and employs a kNN machine learning technique to generate affective labels in the valence-arousal-dominance space. We performed a comparative study of the performance of a content-based recommender (CBR) system for images that uses three types of metadata to model the users and the items: (i) generic metadata, (ii) explicitly acquired affective labels and (iii) implicitly acquired affective labels with the proposed methodology. The results showed that the CBR performs best when explicit labels are used. However, implicitly acquired labels yield a significantly better performance of the CBR than generic metadata while being an unobtrusive feedback tool.

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Facial expression recognition based on two-dimensional discriminant locality preserving projections

Cited by 69 publications

References 6 publications

Facial expression recognition using ensemble of classifiers

Facial expression recognition using ensemble of classifiers

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

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