PROJECT TITLE :

Face detection with a Viola̐Jones based hybrid network - 2017

ABSTRACT:

Face detection is that the determination of the positions and sizes of faces, primarily human, within digital images and videos, often as a element of a broader facial recognition system. It is seen as technologically mature, nonetheless its operational performance usually remains sub-optimal, even at intervals the easier frontal face detection tests. Empirical evidence shows that the Viola-Jones framework, a standard face detection answer with generally superior performance and different fascinating properties, underdetects in some instances. Some true faces survive all however the ultimate stages of the rejection cascade, resulting in missed faces. A hybrid framework consisting of a neural network following a truncated Viola-Jones cascade is made in an try to recover the undetected faces. Presumably, the neural network might fine tune and augment the face decision. Its inputs are a subset of the thresholding (detection) values of a rejection cascade's intermediate stages. Experiments reveal considerably improved performance, with increased detection rates if no false alarm increases are tolerated, with a greater detection rate increase if some false alarm will increase are acceptable, and with a considerable false alarm reduction with no detection reduction. These improved face detection results might address shortcomings in widely-varying applications.