Improving long range and high magnification face recognition: Database acquisition, evaluation, and enhancement

Yao, Yi; Abidi, B.; Kalka, Nathan D.; Schmid, Natalia A.; Abidi, Mongi A.

doi:10.1016/j.cviu.2007.09.004

Cited by 51 publications

(36 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test data set in this paper for outdoor conditions is taken as sequential video under daylight conditions; the superresolution process considers direct sequences of detected faces from the captured frames. The problem with this approach is that under truly difficult conditions, as opposed to the very controlled settings of [16] (full frontal imagery, with a constant inter-ocular distance), it is likely that a collection of detected faces in a direct temporal sequence will not be possible, thus reducing the potential of such algorithms. The work of [7] is more along the lines of what is explored in this paper, including a thorough discussion of the underlying issues that impact algorithm design, as well as an explanation of how to perform realistic controlled experiments under difficult conditions, and algorithmic issues such as predicting when a recognition algorithm is failing in order to enhance recognition performance.…”

Section: Related Workmentioning

confidence: 99%

“…Super-resolution and deblurring were considered in [16] as techniques to enhance images degraded by long distance acquisition (50m -300m), and goes further to show recognition performance improvement for images processed with these techniques compared to the original images. The test data set in this paper for outdoor conditions is taken as sequential video under daylight conditions; the superresolution process considers direct sequences of detected faces from the captured frames.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments

Scheirer

Rocha

Heflin

et al. 2009

2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems

View full text Add to dashboard Cite

Abstract-Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments

Scheirer

Rocha

Heflin

et al. 2009

2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems

View full text Add to dashboard Cite

show abstract

“…Due to a lack of long distance datasets there are not many ways for researchers to evaluate how long ranges effect recognition cores. Currently only one dataset with real subjects has been created by Yao et al [23] -and it is not publicly available. Available sources of data for blur are also lacking.…”

Section: Introductionmentioning

confidence: 99%

Face system evaluation toolkit: Recognition is harder than it seems

Iyer

Scheirer

Boult

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

View full text Add to dashboard Cite

Abstract-Challenges for face recognition still exist in factors such as pose, blur and distance. Many current datasets containing mostly frontal images are regarded as being too easy. With obviously unsolved problems researchers are in need of datasets that test these remaining challenges. There are quite a few datasets in existence to study pose. Datasets to study blur and distance are almost non-existent. Datasets allowing for the study of these variables would prove to be useful to researchers in biometric surveillance applications. However, until now there has been no effective way to create datasets that encompass these three variables in a controlled fashion.Toolsets exist for testing algorithms, but not systems. Designing and creating toolsets to produce a well controlled dataset or to test the full end-to-end recognition system is not trivial. While the use of real subjects may produce the most realistic dataset, it is not always a practical solution and it limits repeatability making the comparison of systems impractical. This paper attempts to address the dataset issue in two ways. The foremost is to introduce a new toolset that allows for the manipulation and capture of synthetic data. With this toolset researchers can not only generate their own datasets, they can do so in real environments to better approximate operational scenarios. Secondly, we provide challenge datasets generated from our validated framework as a first set of data for other researchers. These datasets allow for the study of blur, pose and distance. Overall, this work provides researchers with a new ability to evaluate entire face recognition systems from image acquisition to recognition scores.

show abstract

“…Yao, et al [8] created a face video database, acquired from long distances, high magnifications, and both indoor and outdoor under uncontrolled surveillance conditions. Medioni, et al [9] presented an approach to identify non-cooperative individuals at a distance by inferring 3D shape from a sequence of images.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Long Distance Face Recognition Using Dense - and Sparse-Stereo Reconstruction

Rara

Elhabian

Ali

et al. 2009

Advances in Visual Computing

View full text Add to dashboard Cite

Abstract. This paper introduces a framework for long-distance face recognition using both dense-and sparse-stereo reconstruction. Two methods to determine correspondences of the stereo pair are used in this paper: (a) dense global stereo-matching using maximum-a-posteriori Markov Random Fields (MAP-MRF) algorithms and (b) Active Appearance Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse correspondences. Experiments are performed regarding the use of different features extracted from these vertices for face recognition. A comparison between the two approaches (a) and (b) are carried out in this paper. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces.

show abstract

Improving long range and high magnification face recognition: Database acquisition, evaluation, and enhancement

Cited by 51 publications

References 21 publications

Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments

Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments

Face system evaluation toolkit: Recognition is harder than it seems

A Framework for Long Distance Face Recognition Using Dense - and Sparse-Stereo Reconstruction

Contact Info

Product

Resources

About