Accession Number:

AD1018028

Title:

Robust Face Detection from Still Images

Descriptive Note:

Technical Report

Corporate Author:

DRDC Ottawa Research Centre Ottawa, Ontario Canada

Personal Author(s):

• Laytner,Patrick
• Ling,Chrisford
• Xiao,Qinghan

Report Date:

2014-01-01

Pagination or Media Count:

5.0

Abstract:

Facial recognition is one of the most studied topics in the field of biometrics because of its varied applications. Detection of dark colored faces and poorly illuminated faces are not well studied in the literature due to several challenges. The most critical challenge is that there is inadequate contrast among facial features. To overcome this challenge, a new face detection methodology, which consists of histogram analysis, Haar wavelet transformation and Adaboost learning techniques, is proposed. The extended Yale Face Database B is used to examine the performance of the proposed method and compared against commonly used OpenCVs Haar detection algorithm. The experimental results with 9,883 positive images and 10,349 negative images showed a considerable improvement in face hit rates without a significant change in false acceptance rates.

Descriptors:

• face(anatomy)
• identification systems
• electronic equipment
• images
• detectors
• methodology
• histogram
• algorithms
• illumination
• wavelet transforms
• biometric security
• biometry

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE