Total views : 125

Performance Analysis and Design of Automatic Real Time Face Organs Identification and Classifications


  • Electronics and Communication Engineering, Appa Institute of Engineering and Technology, Gulbarga – 585103, Karnataka, India


Now a day’s security and identification of a person are crucial in real time applications and solving the problems in biometric identification. Objectives: To address the issues in real time, the face and its organ’s identification are the main parts of the human body. The proposed work involves extraction of the face of the real time captured image, after extraction of the face, the organs like eyes, mouth and nose is extracted for identification of exact person. Methods/ Analysis: The present work captures the real time image from cameras or moving video devices for facial recognition using Principal Component Analysis (PCA), the facial features are extracted from both Hidden Markov Model (HMM), Gaussian mixture model (GMM) methods and classified into different organs using Artificial Neural Network (ANN). The features of organs are extracted in different stages, in first stage Eigen values with the help of PCA, second and third stage feature extraction with help GMM and HMM since these three techniques are the most powerful tools for statistical natural image processing. After extraction of organs of the face, the ANN is applied for classifications of eyes, mouth and nose separately. Findings: In the proposed work the facialorgans are separated into threeslight scale images and these are recombined to acquire the appreciationfacial image results like mouth, eyes and nose. The final obtained results shows that the proposed method has been achieved 95.8% recognition accuracy, Fault Rejection Ratio (FRR) is about 93.1% and Fault Acceptance Ratio (FAR) is 1.7 % which are implemented in Matlab2013A.


Facial Database, GMM, HMM, MLP-BP ANN, PCA, Wavelet Franformation

Full Text:

 |  (PDF views: 132)


  • Fulton Bellakhdhar, Kais Loukil.Face Recognition Approach Uses Gabor Wavelets, PCA and SVM.IJCSI International Journal of Computer Science.2013 March;10(2)3:
  • Sangeeta Kakarwal, Ratnadeep Deshmukh.Wavelet Transform based Feature Extraction for Face Recognition. International Journal of Computer Science and Application. 2010.
  • Changhan Park, Joonki Paik.Multimodal Human Verification Using Face and Speech. Vienna, Austria: I-Tech; June 2007.
  • Eshwarappa MN, Mrityunjaya V. Latte.Multimodal Biometric Person Authentication using Speech, Signature and Handwriting Features. International Journal of Advanced Computer Science and Applications. Special Issues in Artificial Intelligence.
  • Palanivel S, Yegnanarayana B.Multimodal Person Authentication is Used Speech, Face and Visual speech. Computer Vision and Image understanding, 20 January 2007.
  • Prabhu Teja G, Ravi S.Face Recognition using Subspace Techniques. Crossref
  • Conard Sanderson, Kuldip K. Paliwal.Noise Compensation in a Person Verification System Using Face and Multiple Speech Features.Journal of the Pattern Recognition Society.
  • Mahesh PK, Shanmukha Swamy MN.A Biometric Identification System based on the Fusion of Palmprint and Speech Signal. IEEE.
  • Kumar R, Baerjee A, Vemuri B C.Trainable Convolution Filters and their Applications to Face Recognition. Biometrics Compendium. IEEE. 2012.
  • Mian AS. Shade Face: Multiple image-based 3D face recognition. Computer Vision Workshops (ICCV Workshops). IEEE 12th International Conference. 2009. DOI: 10.1109/ICCVW.2009.5457505. Crossref
  • Mohammed AA, WuQMJ , Sid-Ahmed, MA. Application of Bidirectional Two-Dimensional Principal Component Analysis to Curvelet Feature Based Face Recognition. IEEE International Conference.2009. DOI: 10.1109/ ICSMC.2009.5346723. Crossref
  • Juefei-Xu F, Savvides M. Subspace-Based Discrete Transform Encoded Local Binary Patterns Representations for Robust Periocular Matching on NIST’s Face Recognition Grand Challenge. IEEE Transactions.23(8).DOI: 10.1109/ TIP.2014.2329460. Crossref
  • TuragaP,Veeraraghavan A, Srivastava A, Chellappa R.Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition Pattern Analysis and Machine Intelligence. IEEE Transactions.33(11).DOI: 10.1109/TPAMI.2011.52. Crossref
  • Elhamifar E,Vidal R. Robust Classification Using Structured Sparse Representation Computer Vision and Pattern Recognition (CVPR). IEEE Conference.2011. DOI: 10.1109/CVPR.2011.5995664. Crossref
  • Farhood Detection in Face Identification Systems Using Zernike Moments and Fresnel Transformation of Facial Images. Indian Journal of Science and Technology.2015 April; 8(8):523–35. DOI: 10.17485/ ijst/2015/v8i8/55787. Crossref
  • Manjunatha Guru VG, Kamalesh VN.Vision Based Human Gait Recognition System: Observations, Pragmatic Conditions and Datasets. Indian Journal of Science and Technology.2015 July;8(15): DOI: 10.17485/ijst/2015/v8i15/71237.Crossref
  • Anuradha K,Sairam N.Spatio-Temporal Based Approaches for Human Action Recognition in Static and Dynamic Background: A Survey. Indian Journal of Science and Technology.2016 February; 9(5): DOI: 10.17485/ijst/2016/ v9i5/72065. Crossref
  • Venkatesan S, Srinivasa Rao Madane S.Study on Identification of Face in Versatile Circumstances through Genetic and Ant Colony Optimization Algorithms. Indian Journal of Science and Technology. 2015 November; 8(30): Crossref
  • Movina R. Ayoob, Mathusoothana R, Kumar S.Face Recognition Using Symmetric Local Graph Structure. Indian Journal of Science and Technology.2015 September; 8(24): DOI: 10.17485/ijst/2015/v8i24/80876. Crossref
  • Jana Selvaganesan, Kannan Natarajan.Robust Face Recognition from Video based on Extensive Feature Set and Fuzzy_Bat Algorithm. Indian Journal of Science and Technology. 2015 December; 8(35): DOI:10.17485/ijst/2015/ v8i35/82202. Crossref
  • Usha Ruby A, George Chellin Chandran J.A Theoretical Approach on Face Recognition with Single Sample Per Class using CS-LBP and Gabor Magnitude and Phase. Indian Journal of Science and Technology.2016 August; 9(31): DOI: 10.17485/ijst/2016/v9i31/85503. Crossref
  • Thai Hoang Le.On Approaching Heuristic Weight Mask to Enhance LBP-based Profile Face Recognition System. Indian Journal of Science and Technology.2016 May; 9(17): 10.17485/ijst/2016/v9i17/92314.
  • Sarath Chandu Gaddam,Ramesh NVK.Attendance Management and User Security System’s based on Eigen Faces Algorithm using Raspberry pi 2 and Ethernet. Indian Journal of Science and Technology.2016 May; 9(17): DOI: 10.17485/ijst/2016/v9i17/92978. Crossref
  • Sunghoon Kim, improved Face Recognition Based on Scale Invariant Feature Transform (SIFT): Training for Integrating Multiple Images and Matching by Key Point’s Descriptor-Geometry. Indian Journal of Science and Technology. 2016 September;9(35): DOI: 10.17485/ijst/2016/ v9i35/101784. Crossref


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.