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Rai, Preeti
- A Development of Efficient Algorithm for Age Detection from Face Images
Abstract Views :322 |
PDF Views:3
Authors
Krati Bakshi
1,
Preeti Rai
1
Affiliations
1 Department of Computer Science, Gyan Ganga Institute of Technology & Science, Jabalpur, IN
1 Department of Computer Science, Gyan Ganga Institute of Technology & Science, Jabalpur, IN
Source
Biometrics and Bioinformatics, Vol 9, No 6 (2017), Pagination: 105-108Abstract
Biometrical system have variety of applications that helps in fields of identification, verification and security etc. face image have many characteristics. Face images shows different aspects of human expressions such as gender, age, emotions, and facial expressions, face color and skin texture etc. Such type of image based face is recognized by different methods and algorithms are applied on image and is widely used in some security purpose, registration process and also identification process or verification processes. Automatic age detection is one of the main issues in pattern recognition which shows the age of human according to her/his facial expressions. This paper presents two feature extraction and for age classification methods. Proposed methods are directional 2DPCA and wavelet methods for feature extraction and for age classification using k-nearest neighbor and multiclass SVM methods.
Keywords
Biometrics, Feature Extraction, Age Classification, FG-NET Database, Two Directional 2DPCA, Wavelet DWT, k-NN and Multiclass SVM.
References
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- Imed Bouchrika Nouzha Harrati Ammar Ladjailia and Sofiane Khedairia, Age Estimation from Facial Images based on Hierarchical Feature Selection, 16thinternational conference on Sciences and Techniques of Automatic control and computer engineering - STA’2015, Monastir, Tunisia, December 21-23, (2015).
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- Gagandeep Kour, Sharad P. Singh “Image Decomposition Using Wavelet Transform”, International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 2 Issue 12 Dec, 2013 Page No. 3477-3480.
- Raj Kumar Sahu, Dr. Yash Pal Singh, Dr. AbhijitKulshrestha, “Facial Expression Recognition by Using Directional 2DPCA”, International Journal of IT, Engineering and Applied Sciences Research (IJIEASR) ISSN: 2319-4413 Volume 4, No. 5, May 2015.
- A Development of Emotion Recognition System
Abstract Views :382 |
PDF Views:3
Authors
Neenu Sharma
1,
Preeti Rai
1
Affiliations
1 Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), IN
1 Gyan Ganga Institute of Technology and Sciences, Jabalpur (M.P.), IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 6 (2017), Pagination: 111-114Abstract
Human Computer Interaction (HCI) is one of most interesting topic in machine visualization and image processing fields. Emotion recognition plays an important role in security and interpersonal communication. Biometric system helps in identification, security and authentication using face image. Recognize emotion of a person from occluded face image is a challenging task in emotion recognition. Feature are calculated for face image using Principe Component Analysis (PCA) and Two-Directional Two Dimension Principal Component Analysis [(2D) 2PCA] along with discrete wavelet transform. K-Nearest Neighbor (K-NN) and multiclass support vector machine used for classification of different emotion. This paper shows the comparative study of feature extraction and classification method. This study is performed in three dataset. JAFFE, CMU and CK database is used for calculating the classification rate of emotion recognition system .Resulting successful classification rate for JAFFE database is 91.8919% for CMU dataset classification rate is 70.339 % and for CK database resulting classification rate is 75.3073% using Multi class support vector machine. Multiclass support vector machine gives better result as compare to K-nearest neighbor.
Keywords
(2D) 2PCA (Two-Directional Two Dimension Principal Component Analysis), Principe Component Analysis (PCA), Multi Class Support Vector Machine (MSVM), K-Nearest Neighbor, JAFFE, CMU, CK Database.References
- Vinícius Silva, Filomena Soares, João S. Esteves,Joana Figueiredo, Celina P. Leão, Cristina Santos, Ana PaulaPereira Real-time Emotions Recognition System. 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).
- Tasnim Tarannum, Anwesha Pauly and Kamrul Hasan Talukder Human Expression Recognition Based on Facial Features 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) .
- I. Kotsia and I. Pitas, “Facial expression recognition in image sequences using geometric deformation features and support vector machines,” IEEE Trans. Image Process., vol. 16, no. 1, pp. 172–187, 2007.
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- A. Garg, and V. Choudhary, Facial Expression Recognition using PrincipleComponent Analysis, (IJSRET) International Journal of Scientific Research Engineering and Technology, Vol. 1, No. 2, July 2012.
- M. Kumbhar, M. Patil and A. Jadhav, Facial Expression Recognitionusing Gabor Wavelet, International Journal of Computer Applications (0975-8887), Vol. 68, No. 23, April 2013.
- Deepjoy Das, Alok Chakrabarty Emotion Recognition from Face Dataset Using Deep Neural Nets Department of Computer Science &Engineering National Institute of Technology Meghalaya Meghalaya, India.
- M. Shamim Hossain1, (Senior Member, IEEE), and Ghulam Muhammad2, (Member, IEEE) Special Section On Emotion-Aware Mobile Computing Digital Object Identifier 10.1109/ACCESS.2017.2672829
- Humaid Alshamsi, Hongying Meng, Maozhen LiReal Time Facial Expression Recognition App Development on Mobile Phones 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).
- Austin Nicolai and Anthony Choi Facial Emotion Recognition 2015 IEEE International Conference on Systems, Man, and Cybernetics Using Fuzzy Systems.
- Fatima Zahra SALMAM, Abdellah MADANI, Mohamed KISSI 13th International Conference Computer Graphics, Imaging and Visualization 2016.
- N. Perveen, S. Gupta, and K. Verma, “Facial expression recognition using facial characteristic points and Gini index,” Engineering and Systems (SCES), Students Conference on. IEEE, 2012.
- A. Saeed, A. Al-Hamadi, R. Niese, and M. Elzobi, “Effective geometric features for human emotion recognition,” Signal Processing (ICSP), 2012 IEEE 11th International Conference on, Vol. 1, pp. pp. 623-627, IEEE, 2012.
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- A Directional Edge Detector Operator to Detect All Type of Edges of Image
Abstract Views :324 |
PDF Views:4
Authors
Priya Singh
1,
Preeti Rai
1
Affiliations
1 Gyan Ganga Institute of Technology and Sciences, Jabalpur, Madhya Pradesh, IN
1 Gyan Ganga Institute of Technology and Sciences, Jabalpur, Madhya Pradesh, IN
Source
Digital Image Processing, Vol 9, No 7 (2017), Pagination: 133-137Abstract
This work proposes a new directional edge detector operator to detect all type of edges of the image. Initially this thesis provides an introduction to digital image processing followed by a review of various edge detection methods. In the research part authors initially did few experiments on the famous Lena image then by these experiments authors come to two conclusions. These conclusions are basic pillars of the proposed work. (a) Image contains smoothly varying area separated by edge information. (b) Edge pixels take intensities comparatively smaller or larger than the surrounding pixels. Based on this conclusion authors have design the simplest method that works on image patches and find edge pixels. . This operator works on information aligned in four different directions. With the help of a mean filter or a given threshold the proposed method is able to detect strong edges of the images.Keywords
Directional Edge Detector Operator.References
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