Apr 092019
 

Bacha‘s paper on “Face detection and tracking using hybrid margin-based ROI techniques” has been published in Springer’s journal Visual Computing. Congratulations to Bacha!

This study is to solve the problem of low accuracy and slow processing speed for real-time face detection and tracking systems. A margin-based region of interest approach with fixed and dynamic margin concepts is proposed to speed up the processing time. In addition, a hybrid system is developed to boost the accuracy and overcome the deficiency of the main detection algorithm. This approach consists of two routines, i.e., main and escape routines. Three algorithms are used independently as the main routine to evaluate the effectiveness of the proposed hybrid approach. These algorithms are Haar cascade, Joint cascade, and multitask convolutional neural networks. The escape routine based on template matching algorithm is designed to evaluate the effectiveness of the proposed hybrid approach and improve detection accuracy. Two RGB video datasets with diversity and variations in face poses, video backgrounds, illuminations, video resolutions, expressions, over exposed faces, and occlusions of people within various unseen environments have been used for experiments and evaluation. The experiment results confirm that the hybrid approach is capable of detecting and tracking faces in non-frontal orientation with better accuracy and faster processing speed, i.e., four times faster than the conventional full frame scanning techniques.

Details of this paper are available here.

Jul 212018
 

Bacha‘s paper on “Using Margin-based Region of Interest Technique with Multi-Task Convolutional Neural Network and Template Matching for Robust Face Detection and Tracking System ” has been accepted in the 2nd International Conference on Imaging, Signal Processing and Communication (ICISPC 2018).

Abstract. Real-time face detection and tracking systems suffer from low accuracy and slow processing speed that lead to poor robustness. This problem is vital in real-time setups including human robot interactions (HRI) and video analysis systems. This paper presents margin-based region of interest (MROI) approach to speed up the processing time. Further a hybrid approach is also presented that combines Multi-task Convolutional Neural Networks (MTCNN) with template matching to improve face detection accuracy. The MROI approach which is responsible to speed up the processing time is presented in two variants with fixed and dynamic margin
concepts. Dataset used in this work comprises of twenty RGB video files. Each video file is fifteen seconds long and been created from real-life videos containing a person in lecture delivering environment. Each video file contains a person in which the person moves, turns head and the camera also moves. The highest face detection and tracking accuracy achieved in this paper is 99.31% with a processing time of 14.93 milliseconds per frame. The proposed hybrid algorithm significantly improves the ability to detect and track faces in sideway orientation apart from frontal face. The proposed algorithm has the ability to process above 65 frames per second (FPS). The system presented has increased FPS processing ability by more than 400% as well as given boost to the accuracy if compared to the conventional MTCNN full frame scanning techniques.

Details of the works for this paper are available here.