A discriminated correlation classifier for face recognition

Han, Zhongkai; Fang, Chi; Ding, Xiaoqing

doi:10.1145/1774088.1774406

Cited by 3 publications

(1 citation statement)

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“…Even the repair must be relative to the contrast observations of input image. Natural capturing images also face the problem of improper or discriminative [3][4][5][6] image capturing. Such capturing results partial capturing [7], pose variation, facial information loss [8] etc.…”

Section: Introductionmentioning

confidence: 99%

MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

Juneja¹

2016

Advanced Computing and Communication Technologies

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Illumination Variation and wearable objects loses the partial facial information that it degrades the accuracy of recognition process. In this paper, a high performance driven accurate method is provided for facial recognition. The proposed MFAST (Multi-Featured Analog Signal Transformed) Model genuinely transmute the substantial facial information in analog featured conformation. This analog featured structured is formed using segmented featured elicitation. These features include center difference evaluation as moment, the asymmetric structure analysis as Skewness and Outlier Prone Measure as Kurtosis. These analogous features are shaped to justified form and generate a compound signal form. Mapping of these distillates signal points over facial dataset with specification of threshold window. The decomposed form recognition method enhanced the accuracy and performance. The experimentation on FERET, LFW and Indian Databases signify that the model outperformed than existing algorithms.

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Section: Introductionmentioning

confidence: 99%

MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

Juneja¹

2016

Advanced Computing and Communication Technologies

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An improved algorithm for face recognition using wavelet and facial parameters

Singh¹,

Sinha²

2010

Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

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A Novel Algorithm for Human Facial Expression Recognition using Gabor Filter and Eigenvector

Dhankhar¹,

Sahu²

2014

IJCA

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Facial Expression Recognition has always been topic of interest for research in field of human computer/machine interaction based on machine learning and image processing tools. However, the inaccuracy of such systems is always challenging. Hence to resolve this problem, a novel algorithm using Gabor Filter and Eigenvector Based Distributed Feature, is proposed in this paper. The work is enhancement of existing Face Identification by covering the challenges of Facial Expression. The approach combines face recognition along with facial expression recognition. Over the time authentication system is replaced by biometric authentication. These systems are based on some human features and behavior. The mean accuracy is improved to 93.4%. The effectiveness and stability is shown in experimental results of the algorithm.

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A discriminated correlation classifier for face recognition

Cited by 3 publications

References 20 publications

MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

An improved algorithm for face recognition using wavelet and facial parameters

A Novel Algorithm for Human Facial Expression Recognition using Gabor Filter and Eigenvector

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