Stopping Wildlife Poaching Using Face Recognition
Rajyashree1, Monojit Debnath2, Sanyam Malik3, Shashank Mishra4
1Rajyashree, Assistant Professor, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Monojit Debnath, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Sanyam Malik, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Shashank Mishra, Department of Computer Science, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 104-110 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3403048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poaching is illegal hunting, killing of wild animals also referred to the illegal harvesting of wild plant species. It’s considered as an ecological wrong doing against the regular assets, unlawful catching of natural life for creature extravagances, for example, ivory, horn, teeth, skin and bone. India is home to some of the most beautiful animals on the planet such as Tiger, Elephant, Rhino, Leopard, Lizard and many types of snakes. These glorious creatures were utilized for games of chasing, presently they are ensured under the wildlife protection act of India. Sadly enough, the population of these beautiful wild animals of India are going down because of Poaching for ivory, horn, teeth and skin. In this project, we proposed to stop the poaching by using face recognition technology for the detection unauthorized persons responsible for poaching. Government has installed several cameras for the purpose of monitoring the wildlife in the forest. We are planning to connect the face recognition to that and with we will be able to detect the person, his location in the forest and an alarm will be sent if the person is not on the database of the forest department. Here we are also planning to update the architecture of the system so the detection will be much better from the previously designed systems. In this paper an algorithm is provided for face detection in noisy background with additional spoof face detection. The implemented algorithms are CNN algorithm, SVM classifier, Local Binary Pattern (LBP), Micro Texture Analysis. For fast face detection the LBP is used. The error face detection rate is decreased using eye detection algorithm. To increase the contrast and orientation the detected facial image is processed with maintaining high face recognition accuracy. Here large dataset of facial and fake images is used to train the dataset. True positive result of this algorithm is 98.8% and the correct facial recognition is 99.2%. Here we have done work on spoof face detection with Micro Texture Analysis with multi scaling LBP. Two databases are used one is Yale database and other is publicly available database.
Keyword: MachineLearning,ImageDetection,Facedetection,LBP,SV Mclassifier, Micro-Texture Analysis,Back-Propagation.
Scope of the Article: Machine Learning