A Comprehensive Survey of Face Databases for Constrained and Unconstrained Environments
Siddheshwar S. Gangonda Prashant P. Patavardhan Kailash J. Karande
Research Scholar, Dept. of E&TC Professor, Dept. of ECE Professor, Dept. of E&TC
SKN Sinhgad COE, Pandharpur, KLS Gogte Institute of Technology, SKN Sinhgad COE, Pandharpur,
Maharashtra, India. Belagavi, Karnataka, India. Maharashtra, India.
Abstract—Face recognition has witnessed a lot of attention due to its numerous applications in many fields like computer vision, security, pattern recognition and computer graphics, but still is a challenging and active research area. In this paper, we have presented a comprehensive survey of face databases for constrained and unconstrained Environments. Face databases are used for the face detection and recognition algorithm testing and they have been designed to evaluate the effectiveness of face recognition algorithms. The paper is focused mostly on novel databases that are freely available for the research purposes. Most of the popular face databases are briefly introduced and compared.
Keywords- face recognition, face database, expression, occlusion.
Over the last few years research in face recognition has moved from 2D to 3D. The need for 3D face data has resulted in the need of 3D databases. In this paper, we first give an introduction of publicly available 2D and 3D face databases for constrained and unconstrained Environments. The existence of many databases demands a quantitative comparison of these databases in order to compare more accurately the performances of the various algorithms available in literature 8,9,10,14,15. The development of algorithms robust to illumination, pose, facial expression, age, occlusion changes requires databases of sufficient size that include carefully controlled variations of these factors. Also, common databases are required to comparatively analyze algorithms.
Presently, there are many databases utilized for facial recognition that vary in lighting conditions, size, pose, expressions, the number of imaged subjects and occlusions. The earliest facial databases mostly consists of frontal images, such as the local data set acquired from 115 people at Brown University utilized in the early works in 1987. Nowadays, the facial databases were seen to capture the variations in pose, lighting, imaging angles, ethnicity, gender and facial expressions. Some of the most recent databases capture the variations in image sizes, compression, occlusions and are gathered from different sources such as social media and web 10, 11.
In this paper, we have presented a comprehensive survey of face databases for constrained and unconstrained environments. Section II describes the overview of various face databases. It focuses mostly on novel databases that are freely available for research purposes. Section III describes some of the recent face databases. Section IV compares the various popular face databases. Finally, Section V concludes the paper.
II. FACE DATABASES
Over the past few decades, a large number of face databases have been designed to analyze the effectiveness of face recognition algorithms. The brief introduction of selected databases is as follows. In most cases the link to database download is provided.
A. The AR database
The AR database 12 is one of the very few databases which contain real occlusions and are open to the public. It consists of more than 4,000 color images of 126 people faces (70 men and 56 women). These images suffer from different variations in facial expressions, lighting conditions and occlusions (i.e., sunglasses and scarves). They were captured under strictly controlled conditions. No restrictions on wear (clothes, glasses, etc.), makeup, hair style, etc. were imposed to subjects. For each subject, 26 images in total were captured in two sessions (two weeks apart) 1.
The limitations of the AR database are that it only contains two types of occlusions, i.e., sunglasses and scarf, and the location of the occlusion is either on the upper face or lower face. This database can be downloaded from the link http://rvl1.ecn.purdue.edu/~aleix/aleix face DB.html 12.
Fig.1 Sample images of two sessions from AR database 2.
B. The Extended Yale B database
It consists of 2,414 frontal face images of 38 persons in 64 different lighting conditions. For every subject in a particular pose, an image with surrounding (background) illumination