Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction

Indumathi, R.; Palanivel, N.

doi:10.1109/icices.2014.7033912

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…There are several wild facial images which have been used as training image after pre-processing. Pre-processing involves removing unwanted background and noise from facial images [3]. A number of face recognition methods have been presented but most of them failed to handle poses, occlusion and illumination problems.…”

Section: Introductionmentioning

confidence: 99%

Face recognition system based on principal components analysis and distance measures

Goyal¹,

Meenpal²

2018

IJET

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Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Face recognition system based on principal components analysis and distance measures

Goyal¹,

Meenpal²

2018

IJET

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“…5) They can't be viewed as vigorous highlights since: a) The force of a pixel is exceptionally delicate to the nearness of clamor in the picture or to enlightenment changes. b) The usage of the impressive number of pixels in the photo in the picture is computationally mind boggling and tedious c) Using all picture pixels does not dispose of any excess data and is along these lines an exceptionally wasteful type of highlight [8]. Hybrid rate ought to be 80%-95% Mutation rate ought to be low The strategy for choice ought to be fitting.…”

Section: H Hidden Markov Models (Hmm) 1)mentioning

confidence: 99%

Various Approaches for Face Recognition and Detection: A Survey

Patel¹

2018

IJRASET

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Face is the personality of a man. The strategy to abuse this physical usefulness has experienced an incredible change since the coming of picture handling strategies. Facial acknowledgment innovation is an essential approach in our day by day lives. It is for the most part used to recognize an individual and give security in different parts of life.It becomes very tough for the researcher to obtain the best facial recognition rate in a different situation and criteria. Facial recognition is the capacity to perceive individuals in light of their facial highlights. Face recognition is a very popular area of computer vision to make applications based on image analysis. Places such as airports, military, universities, banks and ATMs-face detection and recognition plays an important role. There are various methods for face recognition. In this paper, we had discussed various face detection and recognition methods.

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Face recognition algorithm based on wavelet transform and local linear embedding

Zhou

Wang

2018

Cluster Comput

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Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction

Cited by 3 publications

References 6 publications

Face recognition system based on principal components analysis and distance measures

Face recognition system based on principal components analysis and distance measures

Various Approaches for Face Recognition and Detection: A Survey

Face recognition algorithm based on wavelet transform and local linear embedding

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