Face Recognition for Criminal Analysis
Using Haar Classifier

Chethana, H T; TRISILA, DEVI C. NAGAVI

doi:10.26634/jcom.8.1.17390

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the literature survey, it is observed that the majority of the work is available in the field of unconstrained environment [20][21][22][23][24][25] especially in different areas of partial occlusion [1,2,4]. Limited work is reported in the area of partial occlusion in face recognition with constrained environment and achieved a recognition accuracy of 92.38%.…”

Section: Related Workmentioning

confidence: 99%

Detection of Partially Occluded Faces Using Convolutional Neural Networks

Chethana¹,

Trisiladevi²,

Shashank³

2021

Atlantis Highlights in Computer Sciences

View full text Add to dashboard Cite

Partial occlusion in the face refers to the local region of the face with objects such as sunglasses, scarf, hands and beard which leads to loss of information thereby affecting the overall recognition accuracy. It is one of the challenging problems in computer vision. There are many traditional perceptions based models which have become perfect vehicles for identifying partially occluded facial images in unconstrained environments but they fail to be recognized in constrained environments. The images captured under low lighting conditions and noisy situations are called facial images with a constrained environment. The main contribution of this paper is to recognize partially occluded faces using Convolutional Neural Networks (CNN) in a constrained environment. Hence, an attempt is made in this direction to improve the recognition accuracy for partially occluded facial images. Experimental results demonstrated that the proposed system provides a confidence level of 93% and it outperforms the state of art with the other existing partially occluded face recognition algorithms.

show abstract