Analyzing Impact of Image Scaling Algorithms on Viola-Jones Face Detection Framework


Downloads per month over past year

Sharma, H and Saurav, S and Singh, S and Saini, AK and Saini, R (2015) Analyzing Impact of Image Scaling Algorithms on Viola-Jones Face Detection Framework. In: 4th International Conference on Advances in Computing, Communications & Informatics(ICACCI-2015), August 10-13, 2015, SCMS, Aluva, Kochi. (Submitted)

Download (295Kb) | Preview


In today's world of automation, real time face detection with high performance is becoming necessary for a wide number of computer vision and image processing applications. Existing software based system for face detection uses the state of the art Viola and Jones face detection framework. This detector makes use of image scaling approach to detect faces of different dimensions and thus, performance of image scalar plays an important role in enhancing the accuracy of this detector. A low quality image scaling algorithm results in loss of features which directly affects the performance of the detector. Therefore, in this paper we have analyzed the effect of different image scaling algorithms existing in literature on the performance of the Viola and Jones face detection framework and have tried to find out the optimal algorithm significant in performance. The algorithms which will be analyzed are: Nearest Neighbor, Bilinear, Bicubic, Extended Linear and Piece-wise Extended Linear. All these algorithms have been integrated with the Viola and Jones face detection code available with OpenCV library and has been tested with different well know databases containing frontal faces.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Image Scaling; Viola and Jones face detection framework; Haar cascade classifiers; OpenCV
Subjects: Semiconductor Devices > IC Design
Divisions: Semiconductor Devices
Depositing User: Mr. Jitendra Nath Bajpai
Date Deposited: 12 Jan 2017 09:19
Last Modified: 12 Jan 2017 09:19

Actions (login required)

View Item View Item