Race classification using YOLO based on human face | |
| Author | Malisetty, Reethi |
| Call Number | AIT Thesis no.ISE-19-41 |
| Subject(s) | Deep learning (Machine learning) Image processing|xMathematics Neural networks (Computer science) |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Microelectronics and Embedded systems, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | |
| Abstract | Nowadays, machine learning has found a wide range of applications in many fields. Deep learning techniques especially Convolutional Neural Networks are most commonly applied to analysing visual imagery. Image recognition, face recognition, object recognition, speech recognition etc., are the major applications of the CNN. We utilize the advances in deep learning to build a system for face detection and test it on challenging database. The proposed model describes the development of computer vision system in detection and classification of faces in the images. In the proposed model we are using YOLO architecture for detecting the faces of persons in the images. The proposed model detects the faces using bounding boxes and further classifies the identified faces for different races like Asians, Europeans, Africans, Americans etc. The experimental results indicate that the proposed yolo model is efficient and most successful network configuration. |
| Year | 2019 |
| Corresponding Series Added Entry | |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Mongkol Ekpanyapong; |
| Examination Committee(s) | Dailey, Matthew N. ;Huynh Trung Luong; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Thesis (M. Eng.) -- Asian Institute of Technology, 2019 |