Open source biometric recognition

Klontz, Joshua C.; Klare, Brendan; Klum, Scott; Jain, Anil K.; Burge, Mark J.

doi:10.1109/btas.2013.6712754

Cited by 83 publications

(51 citation statements)

References 22 publications

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“…The FaceSketchID System uses OpenCV [36] as a matrix library, Eigen [37] for statistical learning, and Qt [38] for the GUI. Some modules of the FaceSketchID System are available in OpenBR [39]. On a 2.9 GHz Intel Core i7 laptop with 8 GB of RAM, enrollment (including eye detection) and matching speeds are 1.07 templates per second per thread and about 22,000 comparisons per second per thread, respectively.…”

Section: The Facesketchid Systemmentioning

confidence: 99%

The FaceSketchID System: Matching Facial Composites to Mugshots

Klum

Han

Klare

et al. 2014

IEEE Trans.Inform.Forensic Secur.

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Abstract-Facial composites are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. These composites, generated from witness descriptions, are posted in public places and in the media with the hope that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow, tedious, and may not lead to the timely apprehension of a suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. Because of this requirement, facial composite recognition is an important topic for biometrics researchers. While substantial progress has been made in nonforensic facial composite (or viewed composite) recognition over the past decade, very little work has been done using operational composites relevant to law enforcement agencies. Furthermore, no facial composite to mugshot matching systems have been documented that are readily deployable as standalone software. Thus, the contributions of this paper include: (i) an exploration of composite recognition use cases involving multiple forms of facial composites, (ii) the FaceSketchID System, a scalable and operationally deployable software system that achieves stateof-the-art matching accuracy on facial composites using two algorithms (holistic and component-based), and (iii) a study of the effects of training data on algorithm performance. We present experimental results using a large mugshot gallery that is representative of a law enforcement agency's mugshot database. All results are compared against three state-of-the-art commercial-off-the-shelf (COTS) face recognition systems.

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Section: The Facesketchid Systemmentioning

confidence: 99%

The FaceSketchID System: Matching Facial Composites to Mugshots

Klum

Han

Klare

et al. 2014

IEEE Trans.Inform.Forensic Secur.

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“…There have been attempts to foment reproducibility of research results in the biometric community with the release of public software [20,23,31,32] and datasets [15,16,33,34]. Various biometric communities organize open challenges [35,36], for which web-based solutions for data access and result posting are particularly attractive [37].…”

Section: Reproducible Research In Biometricsmentioning

confidence: 99%

“…On one hand, OpenBR [32] is an open source C++ library of algorithms to perform biometric recognition experiments. Unfortunately, this library only has a limited set of algorithms and biometric databases, which it can evaluate.…”

Section: Reproducible Research In Biometricsmentioning

confidence: 99%

Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Günther

Shafey

Marcel

2016

Face Recognition Across the Imaging Spectrum

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One important type of biometric authentication is face recognition, a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted towards unconstrained "in-the-wild" still images and videos. To some extent, current state-of-the-art systems are able to cope with variability due to pose, illumination, expression, and size, which represent the challenges in unconstrained face recognition. To date, only few attempts have addressed the problem of face recognition in mobile environment, where high degradation is present during both data acquisition and transmission. This book chapter deals with face recognition in mobile and other challenging environments, where both still images and video sequences are examined. We provide an experimental study of one commercial of-the-shelf and four recent open-source face recognition algorithms, including color-based linear discriminant analysis, local Gabor binary pattern histogram sequences, Gabor grid graphs and inter-session variability modeling. Experiments are performed on several freely available challenging still image and video face databases, including one mobile database, always following the evaluation protocols that are attached to the databases. Finally, we supply an easily extensible opensource toolbox to re-run all the experiments, which includes the modeling techniques, the evaluation protocols and metrics used in the experiments, and provides a detailed description on how to re-generate the results.

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“…Subsequently the Linear Discriminant Analysis (LDA) is performed in order to create the feature vector of the size equal to 768 elements. Number of features was chosen upon an analysis of the previous research studies (Klontz et al 2013;Jin and Zhang 2014). The samples of face landmarks (mouth, jaw, chin) found for 3 different persons together with corresponding first 6 values of feature vectors of the mouth region were presented in Fig.…”

Section: Face Biometrymentioning

confidence: 99%

Validating data acquired with experimental multimodal biometric system installed in bank branches

et al. 2017

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An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.

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Open source biometric recognition

Cited by 83 publications

References 22 publications

The FaceSketchID System: Matching Facial Composites to Mugshots

The FaceSketchID System: Matching Facial Composites to Mugshots

Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Validating data acquired with experimental multimodal biometric system installed in bank branches

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