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FDGRS: Improve Accuracy of Face Detection and Gender Recognition in Complex Lighting Environment by Image Enhancement Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 29 |
| Year of Publication: 2018 |
| Authors: Reenu Rani, Subhash Chandra Jat |
10.5120/ijca2018918122
|
Reenu Rani, Subhash Chandra Jat . FDGRS: Improve Accuracy of Face Detection and Gender Recognition in Complex Lighting Environment by Image Enhancement Techniques. International Journal of Computer Applications. 182, 29 ( Nov 2018), 6-14. DOI=10.5120/ijca2018918122
Abstract
This work focuses on the area of face processing and aims at designing a reliable framework to facilitate face, and gender recognition. Gender recognition aims at recognizing a person’s gender (Male/Female). Automatic Gender recognition has become relevant to an increasing amount of applications gender recognition by face such as human computer interaction systems, content based image search, video surveillance, and more. Extensive experiment shows that the proposed model is able to capture both global and local information about faces. Tasks perform by system algorithm face detection, histogram of that particular for getting image information, reduce the image information by using histogram equalization and adaptive histogram equalization, noise remove by proposed NRCustom filter, human face characteristic extraction system and finally feature vector matching, classification. For experiment, purpose we adopted colored face image with different complex lighting condition (dark/dim/shaded) of random size and perform their simulation. Then finally, the system classifies the gender (Male/Female), count total no of images in database and separate number of males and females are present in the database.
References
- Mahmoud Afifia,b, Abdelrahman Abdelhamed” AFIF4: Deep Gender recognition based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces”, Elsevier, November 21, 2017.
- P. Comon, “Independent component analysis, a new concept?,” Signal Processing, vol. 36, no. 3, pp. 287-314, 1994.
- Huaining Cheng et. Al,” gender and ethnicity classificationfrom small subsets of human body measurements”, AFRL-RH-WP-TP-2014-0014.
- A. R . Ardakany, M ember, IACSIT and A. M. Jou la,” Gender Recognition Based on Edge Histogram”, International Journal of Computer Theory and Engineering Vol. 4, No. 2, April 2012.
- MandaVema Reddy,” Face and Facial Expression Detection Using Viola-Jones and PCA Algorithm”, Intenation journal and magazine of engineering, technology, management and research, ISSN No. 2348-4845.
- Yingxiao Wu, Yan Zhuang,et.al,” Human Gender recognition: A Review”, IEEE SENSORS JOURNAL, VOL. X, NO. X, XXXXXXX 2015.
- B Prabhavathi, V Tanuja, V Madhu Viswanatham and M Rajashekhara Babu,” A smart technique for attendance system to recognize faces through parallelism”, IOP Conf. Series: Materials Science and Engineering 263 (2017) 042095 doi:10.1088/1757-899X/263/4/042095.
- Eunsung L., Sangjin K., Wonseok K., Doochun S., and Joonki P. January 2014. Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images IEEE Geoscience and Remote Sensing Letters. Vol.10, No.1.
- W. Gao, B. Cao, S. Shan, D. Zhou, X. Zhang, and D. Zhao, “The CAS-PEAL largescale Chinese face database and baseline evaluations,” Technical Report No. JDLTR_04_FR_001, Joint Research & Development Laboratory, CAS, 2004.
- Arun Kumar Nagdeve, Somesh Kumar Dewangan,” automated facial feature points extraction”, International Journal of Computer and Electronics Research [Volume 1, Issue 3, October 2012.
- A. Hadid, M. Pietikainen, and T. Ahonen, “A discriminative feature space for detecting and recognizing faces,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2004, pp. II-797-II-804, vol. 2.
- Yujie Dong, Damon L.Woodard,”Eyebrow Shape-Based Features for biometric recognition and gender classification”, 978-1-4577-1359-0/11/$26.00 ©2011 IEEE.
- A. Ross, K. Nandakumar, and A. K. Jain, "Introduction to Multibiometrics," Handbook of Biometrics, A. Jain, P. Flynn and A. Ross, eds., pp. 271-292: Springer US, 2008.
- Ramin Azarmehr, Robert Laganiere, Won-Sook Lee, Christina Xu, and Daniel Laroche. Real-time embedded age and gender recognition in unconstrained video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 57–65, 2015.
- M. M. Rahman, S. Rahman, M. Kamal, M. Abdullah-AlWadud, E. K. Dey, and M. Shoyaib. Noise adaptive binary pattern for face image analysis. In 2015 18th International Conference on Computer and Information Technology (ICCIT), pages 390–395, 2015.
- Wenhao Zhang, Melvyn L Smith, Lyndon N Smith, and Abdul Farooq, “Gender recognition from facial images: two or three dimensions” JOSA A, 33:333–344, 2016.
- Caifeng Shan. Learning local binary patterns for gender recognition on real-world face images. Pattern Recognition Letters, 33:431–437, 2012.
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