Effectiveness of Eigenspaces for Facial Expressions Recognition

Murthy, G. R. S.; Jadon, R. S.

doi:10.7763/ijcte.2009.v1.103

Cited by 39 publications

(36 citation statements)

References 3 publications

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“…In our opinion, the difference might have happened because the students observe the image differently and simultaneously achieve more emotions (anger and disgust). The affirmation is in accordance with Murthy [35] and Zhang [53] -the most difficult emotion to mimic accurately is fear and this emotion is processed differently from other basic facial emotions. According to various researches [35] the three emotions sad, disgust, and angry are difficult to distinguish from each other and are therefore often wrongly classified.…”

Section: Percentage Software Successfulness -Comparison Of Resultsupporting

confidence: 66%

Evaluating the Emotional State of a User Using a Webcam

Magdin¹,

Turčáni²,

Hudec³

2016

IJIMAI

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-In online learning is more difficult for teachers identify to see how individual students behave. Student's emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student's performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student's learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner's facial expressions and verbalizations. The majority of current software does not work in real time -scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states.

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Section: Percentage Software Successfulness -Comparison Of Resultsupporting

confidence: 66%

Evaluating the Emotional State of a User Using a Webcam

Magdin¹,

Turčáni²,

Hudec³

2016

IJIMAI

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show abstract

“…Various state of art on facial expression recognition system was made by Bettadapura in [14]. One of the most popular and old subspace methods such as principal component analysis (PCA) [15][16][17][18][19][20] has been used in this work for projection of Fisher linear discrimiant subspace [38][39][40][41][42]. Struc and Pavesic [21] worked on Gabor filter based feature extraction by considering magnitude and phase parts separately for face recognition application.…”

Section: Related Workmentioning

confidence: 99%

“…4. Middle points of the lower and upper lip were marked as P 15 and P 16 . Some of the expressions are exhibited based on compression and expansion of nostrils position.…”

Section: Proposed Frame Workmentioning

confidence: 99%

“…Final eigen vectors corresponding to maximum eigenvalue of the generalized eigenvalue problem is obtained from (15) and given as…”

Section: A Subspace Formationmentioning

confidence: 99%

“…Hence it would be difficult to maintain larger variability between two classes. Optimization for the singularity matrix problems were noticed by Belhumeur et al [7] and proposed a Fisherface method (FF), which uses old and more useful principal component analysis (PCA) [15] based subspace projection strategy. Li et al [8] did discriminant analysis based non parametric approach recognition.…”

Section: Introductionmentioning

confidence: 99%

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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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