Benchmark studies on face recognition (original) (raw)

A Study of Face Databases used as Benchmarks in Face Recognition

Face recognition has become one of the robust means of authentication and hence lots of research has been carried on in this regard. For any face recognition system, the availability of a standard database consisting of appropriate face image samples is very important, since it serves as a benchmark for testing and comparing the results directly for the face recognition algorithms. From the last few decades, the creation of face database by proper acquisition of face images, has been an interesting research topic among research community. While there are many face databases available, the appropriate choice should be based on the task given (age, lighting, poses, expression, etc.). This paper makes a scrutinizing study of the existing face databases. The aim here is to give a clear picture to the researchers regarding the selection of the face databases to build effective face recognition systems.

INFACE and EPOCH database: A Benchmark for Face Recognition in Uncontrolled conditions

Research Journal of Applied Sciences, Engineering and Technology

The main focus of our study is to build a database of labeled face images display with wide variations in pose, lighting, appearance and age. Recognizing human faces amid natural setting is emerging as a critically important and technically challenging computer vision problem. Most of the previous cases of analysis to study the specific variations of the face recognition problem focused on recognition of faces captured under controlled environment in standard laboratory setting. These variations include position, pose, lighting, background, camera quality and gender. But in real environment, there are innumerable applications in which there is little or no control over such variations. In this study, we introduce two novel database viz. "INFACE" and "EPOCH", which can effectively contribute to the face recognition research, in general and suited to the Indian setting (appearance, expressions, etc.) in particular. The former data base ("INFACE"), includes around 7400 face images of 37 individuals collected from approximately 45 Indian movies. The images of later data base ("EPOCH") portray the variations with age. It contains 1000 images of 10 individuals spanning four age groups. The data base specimens of INFACE have high degree of variability (in scale, location, orientation, pose, expression, illumination, appearance and degree of occlusion) which one could ever see in natural world. EPOCH database is providing a large set of unconstrained face images which includes age variance of an individual. In addition to the individuality of the face, these databases provide additional information on pose, gender, expression, location etc. Specific annotations for each image have been stored as XML for easy retrieval. These databases will be made public to catalyze research and development for accurate face recognition.

Overview of the face recognition grand challenge

2005

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The Face Recognition Grand Challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery. * Please direct correspondence to Jonathon Phillips, jonathon@nist.gov 1 This paper discusses ver2.0 of the FRGC. Ver1.0 was a small challenge problem designed to introduce researchers to the FRGC challenge problem protocol, procedures, and data formats.

The ENSIB database : a benchmark for face recognition

2007

We present in this paper a benchmark for face recognition. The database has been created with first year students of an engineer school and it is composed of 100 individuals with 40 different views. The principal interest is that we will be able to acquire new data of the same individuals in the future taking into account possible appearance changes. We then use this database to test one algorithm for face recognition.

Reimagining the Central Challenge of Face Recognition: Turning a Problem Into an Advantage

Pattern Recognition, 2018

High inter-personal similarity has been universally acknowledged as the principal challenge of auto-3 matic face recognition since the earliest days of research in this area. The challenge is particularly 4 prominent when images or videos are acquired in largely unconstrained conditions 'in the wild', and 5 intra-personal variability due to illumination, pose, occlusions, and a variety of other confounds is 6 extreme. Counter to the general consensus and intuition, in this paper I demonstrate that in some 7 contexts, high inter-personal similarity can be used to advantage, i.e. it can help improve recogni-8 tion performance. I start by a theoretical introduction of this key conceptual novelty which I term 9 'quasi-transitive similarity', describe an approach that implements it in practice, and demonstrate its 10 effectiveness empirically. The results on a most challenging real-world data set show impressive per- 11 formance, and open avenues to future research on different technical approaches which make use of 12 this novel idea. 13 14 dissimilarity. 15 16

A Benchmark and Comparative Study of Video-based Face Recognition on COX Face Database

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2015

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: Videoto- Still (V2S), Still-to-Video (S2V) and Video-to-Video (V2V), respectively taking video or still image as query or target. To our best knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX1 Face DB. Specifically, we make three contributions. Firstly, we collect and release a large scale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V and V2V). Secondly, for benchmarking the thr...

An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms

Lecture Notes in Computer Science, 2012

In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid prototyping of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processing and machine learning library Bob. It uses well-specified face recognition protocols to ensure that results are comparable and reproducible. We show that the face recognition algorithms implemented in Bob as well as third party face recognition libraries can be used to run face recognition experiments within the framework of the facereclib. As a proof of concept, we execute four different state-of-the-art face recognition algorithms: local Gabor binary pattern histogram sequences (LGBPHS), Gabor graph comparisons with a Gabor phase based similarity measure, inter-session variability modeling (ISV) of DCT block features, and the linear discriminant analysis on two different color channels (LDA-IR) on two different databases: The Good, The Bad, & The Ugly, and the BANCA database, in all cases using their fixed protocols. The results show that there is not one face recognition algorithm that outperforms all others, but rather that the results are strongly dependent on the employed database.