000			02541nam a22002057a 4500
003			OSt
005			20220107122851.0
008			190621b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aMuhammed Hassan M (91617011) _915918
245			_aAI based facial recognition for unique identification of animals
300			_aMSC MI 2017-2019
500			_aI am part of a project that has the overall scope as Animal face detection and recognition using deep learning techniques.Animal Facial Recognition is a fairly complex task as it would require using a unique identier that distinguishes one animal in a species from another. This will involve identifying unique characteristics for an animal that makes it identiable. The possibilities being explored include colour, texture, face geometry and birth patterns to uniquely identify an animal from all four sides (front, back and two sides). The animals being considered are cows, cats, dogs and rabbits.The scope of my project work is to extract features to determine the unique geometry of the frontal face. To extract the face geometry, identifying facial landmarks of the selected animals was the rst step. This is something that has to be built from ground up as facial landmark denitions of these animals are not available in public domain.I started by trying out a face landmark detection with simple neural net and annotated landmarks in a CSV le. While this technique works well with human face detection, the results with animals were not as expected. I then tried Mask RCNN based approach. M-RCNN is a state-of-the-art model for object detection. It is a deep neural network aimed to solve instance segmentation problem in machine learning or computer vision.I have now created a model - Mask-Region based Convolutional Neural Network(M-RCNN) for identifying the facial landmarks like eyes, muzzle etc. of cattle.The methodology included creating an annotated landmark data for training and building a model for testing.It only takes the region of interest within an object and these regions will be passed to the further neural network to give the masked region of the object. For pre-processing of images and feature extraction, several methods including Template Matching, Canny Edge Detection, transformation etc. were employed.
502			_bMSC MI _c2017-2019 _dINT _eJayachandran M B
650			_aANIMAL FACIAL RECOGNITION _915919
650			_aDEEP LEARNING _915920
650			_aM-RCNN _915921
650			_aARTIFICIAL INTELLIGENCE _915922
942			_2ddc _cPR
999			_c6512 _d6512