Experimental Analysis of Classification of Facial Images

Malkauthekar, Mahananda D.; Sapkal, Shubhangi; Kakarwal, S. N.

doi:10.1109/iadcc.2009.4809166

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…M. D. Malkauthekar and S. D. Sapkal [26] Presented experimental analysis of classification of facial images. Facial images of different expressions and angles of two classes and three classes are used for classification.…”

Section: Introductionmentioning

confidence: 99%

Subjective Age Prediction of Face Images Using PCA

Tin¹

2012

IJIEE

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This paper details with the subjective age prediction of face images using Principal Component Analysis (PCA). The face database is built by the seven individual age groups which are divided from the adult facial images between 10 to 60 years old. An age prediction algorithm is developed for examining the age of individual. Age prediction is concerned with the use of a training set to train a model that can predict the age of the facial images. The facial feature is extracted based on the geometric feature based method and principal component analysis (PCA) method. The accuracy of the system is analyzed by the variation on the range of the age groups. The efficiency of the system can be confirmed through the experimental results.

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Section: Introductionmentioning

confidence: 99%

Subjective Age Prediction of Face Images Using PCA

Tin¹

2012

IJIEE

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“…Yu S. et al (2009) applied feature vector, which consisted of Euclidean distance, curve distance, angle and volume to recognize a human face. Liwei W. et al (2005) presented IMage Euclidean distance (IMED)to apply to all image recognition, which included face recognition[8,9,10,11,12,13].…”

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confidence: 99%

Thai Buddhist Sculpture Recognition System (TBuSRS)

Pornpanomchai¹,

Arpapong²,

Iamvisetchai³

et al. 2011

IJET

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The objective of this research is to develop a computer system that can recognize some Thai Buddhist sculptures. The system is called "Thai Buddhist sculpture recognition system (TBuSRS)". The TBuSRS consists of five components, which are 1) image acquisition, 2) image preprocessing, 3) feature extraction, 4) image recognition, and 5) display result. In the image acquisition component, the Buddhist sculpture picture is taken by a digital camera. In the image preprocessing component, the Buddhist statue image is enhanced for easy extraction of the Buddhist features. In the feature extraction component, the TBuSRS extracts 14 features used for recognizing a Buddhist sculpture. In the image recognition component, the Euclidean distance technique is applied to recognize the Buddhist sculpture image. In the display result component, a graphic user interface (GUI) is created for displaying the recognition results. The TBuSRS trained the database with 50 kinds of Buddhist sculptures with a total of 500 images. The experiments were conducted on 50 kinds of Buddhist statue images, with a total of 500 images for testing a training data set, and 21 kinds of Buddhist statue image with a total of 105 images for testing an un-training data set, with the precision rates of training and un-training data set of 90.00 percent and 72.38 percent, respectively. The average access time of the system is around 2.72 second per image.

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Gender classification using face recognition

Bissoon

Viriri

2013

2013 International Conference on Adaptive Science and Technology

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Experimental Analysis of Classification of Facial Images

Cited by 4 publications

References 2 publications

Subjective Age Prediction of Face Images Using PCA

Subjective Age Prediction of Face Images Using PCA

Thai Buddhist Sculpture Recognition System (TBuSRS)

Gender classification using face recognition

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