Automatic recognition and analysis of human faces and facial expressions: a survey

Samal, Ashok; Iyengar, Prasana A.

doi:10.1016/0031-3203(92)90007-6

Cited by 784 publications

(303 citation statements)

References 14 publications

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“…Several papers have been published in the area of face emotion recognition for example Samal and Iyengar [9] focussed on early approaches to face recognition. Valentin et al [10] were focussing on neural networks; Chellappa et al [11] were focussing on psychophysics issues with respect to face recognition; Zhang et al [12] focussing on Eigen face, neural networks and elastic matching.…”

Section: Background Literaturementioning

confidence: 99%

Facial emotion detection technology: concept, marketing applications, business drivers and challenges

Joshi¹,

Damle²,

Kumar³

2018

IJET

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The manuscript should contain an abstract. The abstract should be self-contained and citation-free and should not exceed 200 words. The abstract should state the purpose, approach, results and conclusions of the work. The author should assume that the reader has some knowledge of the subject but has not read the paper. Thus, the abstract should be intelligible and complete in it-self (no numerical references); it should not cite figures, tables, or sections of the paper. The abstract should be written using third person instead of first person.

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Section: Background Literaturementioning

confidence: 99%

Facial emotion detection technology: concept, marketing applications, business drivers and challenges

Joshi¹,

Damle²,

Kumar³

2018

IJET

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“…Face recognition is one of current most challenging applications in the pattern recognition literature [4,23,30,31,35]. In this work, the algorithms are evaluated with two widely used face databases, UMIST [8] and FERET [19].…”

Section: Face Recognition Face Recognition Evaluation Designmentioning

confidence: 99%

Kernel Discriminant Learning with Application to Face Recognition

Venetsanopoulos

Rogers

2005

Support Vector Machines: Theory and Applications

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Abstract. When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer from two problems: 1) small training sample size compared to the dimensionality of the sample (or mapped kernel feature) space, and 2) high computational complexity. In this chapter, we introduce a new kernel discriminant learning method, which attempts to deal with the two problems by using regularization and subspace decomposition techniques. The proposed method is tested by extensive experiments performed on real face databases. The obtained results indicate that the method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel Principal Component Analysis and kernel Linear Discriminant Analysis, at a significantly reduced computational cost.

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“…Recent work in image segmentation includes stochastic model-based approaches [25,52], morphological watershed-based region growing [40], energy diffusion [28] and graph partitioning [44].…”

Section: Unsupervised Color-texture Segmentationmentioning

confidence: 99%

“…These restrictions are being eventually revoked in the literature [36,3], since authors deal more and more with realistic environments, while keeping in mind pioneering works in the field [19,40,5,21,6].…”

Section: Introductionmentioning

confidence: 99%

A modular approach to facial feature segmentation on real sequences

Votsis

Drosopoulos

Kollias

2003

Signal Processing: Image Communication

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In this paper a modular approach of gradual confidence for facial feature extraction over real video frames is presented. The problem is being dealt under general imaging conditions and soft presumptions. The proposed methodology copes with large variations in the appearance of diverse subjects, as well as of the same subject in various instances within real video sequences. Areas of the face that statistically seem to be outstanding form an initial set of regions that are likely to include information about the features of interest. Enhancement of these regions produces closed objects, which reveal-through the use of a fuzzy system-a dominant angle, i.e. the facial rotation angle. The object set is restricted using the dominant angle. An exhaustive search is performed among all candidate objects, matching a pattern that models the relative position of the eyes and the mouth. Labeling of the winner features can be used to evaluate the features extracted and provide feedback in an iterative framework. A subset of the MPEG-4 facial definition or facial animation parameter set can be obtained. This gradual feature revelation is performed under optimization for each step, producing a posteriori knowledge about the face and leading to a step-by-step visualization of the features in search. r

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Automatic recognition and analysis of human faces and facial expressions: a survey

Cited by 784 publications

References 14 publications

Facial emotion detection technology: concept, marketing applications, business drivers and challenges

Facial emotion detection technology: concept, marketing applications, business drivers and challenges

Kernel Discriminant Learning with Application to Face Recognition

A modular approach to facial feature segmentation on real sequences

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