Facial soft biometric features for forensic face recognition

This is the author’s version of a work that was accepted for publication in Forensic Science International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International, VOL 257, (2015) DOI 10.1016/j.forsciint.2015.09.002This paper proposes a functional feature-based approach useful for real forensic caseworks, based on the shape, orientation and size of facial traits, which can be considered as a soft biometric approach. The motivation of this work is to provide a set of facial features, which can be understood by non-experts such as judges and support the work of forensic examiners who, in practice, carry out a thorough manual comparison of face images paying special attention to the similarities and differences in shape and size of various facial traits. This new approach constitutes a tool that automatically converts a set of facial landmarks to a set of features (shape and size) corresponding to facial regions of forensic value. These features are furthermore evaluated in a population to generate statistics to support forensic examiners. The proposed features can also be used as additional information that can improve the performance of traditional face recognition systems. These features follow the forensic methodology and are obtained in a continuous and discrete manner from raw images. A statistical analysis is also carried out to study the stability, discrimination power and correlation of the proposed facial features on two realistic databases: MORPH and ATVS Forensic DB. Finally, the performance of both continuous and discrete features is analyzed using different similarity measures. Experimental results show high discrimination power and good recognition performance, especially for continuous features. A final fusion of the best systems configurations achieves rank 10 match results of 100% for ATVS database and 75% for MORPH database demonstrating the benefits of using this information in practice.This work has been partially supported by Spanish Guardia Civil, projects Bio-Shield (TEC2012-34881) from Spanish MINECO and BEAT (FP7-SEC-284989) from EU, and Catedra UAM Telefonica

Roman portraiture and biometric identification

This project utilised three-dimensional scanning technology in the study of ancient Roman art and archaeology: Roman representations of faces executed in marble. In the cultural heritage sector, three-dimensional (3D) scanning finds its primary application in documenting and reconstructing objects and structures mostly of simple geometry: bones, pottery, architecture or the imprint of whole archaeological sites (Adolf 2011). In forensic science, the face is interesting from investigative and probative perspectives, including both recognition and identification. Biometric methods of facial recognition have been part of a plethora of computer science-based applications used in the verification of identity (Davy et al. 2005, Goodwin, Evison and Schofield 2010). The aim of this initial project is to provide objective relevant measurements of key facial features from the two ancient Roman portrait statue three-dimensional scans, which will allow the delineation of relationships between individual portraits including formal and stylistics aspects. The work described in this paper proposal is truly multidisciplinary, it touches on many fields including : Classical archaeologies (specifically ancient art history in the period of the Roman Empire 31BC - AD400), Forensic Anthropology (specifically physical anthropology and human osteology, Facial Biometrics (specifically uniquely recognising humans based upon their intrinsic physical traits and features) and Computer Science and Statistics (specifically the analysis of large complex multi-dimensional data sets)

Roman portraiture and biometric identification

This project utilised three-dimensional scanning technology in the study of ancient Roman art and archaeology: Roman representations of faces executed in marble. In the cultural heritage sector, three-dimensional (3D) scanning finds its primary application in documenting and reconstructing objects and structures mostly of simple geometry: bones, pottery, architecture or the imprint of whole archaeological sites (Adolf 2011). In forensic science, the face is interesting from investigative and probative perspectives, including both recognition and identification. Biometric methods of facial recognition have been part of a plethora of computer science-based applications used in the verification of identity (Davy et al. 2005, Goodwin, Evison and Schofield 2010). The aim of this initial project is to provide objective relevant measurements of key facial features from the two ancient Roman portrait statue three-dimensional scans, which will allow the delineation of relationships between individual portraits including formal and stylistics aspects. The work described in this paper proposal is truly multidisciplinary, it touches on many fields including : Classical archaeologies (specifically ancient art history in the period of the Roman Empire 31BC - AD400), Forensic Anthropology (specifically physical anthropology and human osteology, Facial Biometrics (specifically uniquely recognising humans based upon their intrinsic physical traits and features) and Computer Science and Statistics (specifically the analysis of large complex multi-dimensional data sets)

A New Way for Face Sketch Construction and Detection Using Deep CNN

Traditional hand-drawn face sketches have encountered speed and accuracy issues in the field of forensic science when used in conjunction with contemporary criminal identification technologies. To close this gap, we provide a ground-breaking research article that is built on a stand-alone program that aims to revolutionize the production and identification of composite face sketches. This ground-breaking approach does away with the requirement for forensic artists by enabling users to easily create composite sketches using a drag-and-drop interface. Utilizing the power of deep learning and cloud infrastructure, these generated sketches are seamlessly cross-referenced against an enormous police database to identify suspects quickly and precisely. Our research study offers a dual-pronged approach to combating the rise in criminal activity while using the quick breakthroughs in artificial intelligence. First, we demonstrate how a specific Deep Convolutional Neural Network model transforms sketches of faces into photorealistic photographs. Second, we employ transfer learning for precise suspect identification using the pre-trained VGG-Face model. Utilizing Convolutional Neural Networks, which are famous for their data processing powers and hierarchical feature extraction, is a key component of our strategy. This approach exceeds current methods and boasts an extraordinary average accuracy of 0.98 in identifying people from sketches, providing a crucial tool for strengthening and speeding up forensic investigations. A unique Convolutional Neural Network framework that demonstrates significant improvements over state-of-the-art techniques is also revealed as we dive into the challenging task of matching composite sketches with corresponding digital photos. Our thorough analysis shows the framework to be remarkably accurate, constituting a substantial advance in the field of forensic face sketch production and recognition

Face recognition, a landmarks tale

Face recognition is a technology that appeals to the imagination of many people. This is particularly reflected in the popularity of science-fiction films and forensic detective series such as CSI, CSI New York, CSI Miami, Bones and NCIS.\ud \ud Although these series tend to be set in the present, their application of face recognition should be considered science-fiction. The successes are not, or at least not yet, realistic. This does, however, not mean that it does not, or will never, work. To the contrary, face recognition is used in places where the user does not need or want to cooperate, for example entry to stadiums or stations, or the detection of double entries into databases. Another important reason to use face recognition is that it can be a user-friendly biometric security.\ud \ud Face recognition works reliably and robustly when there is little variance in pose in the images used. In order to eliminate variance, the faces are aligned to a reference. For this we will use a set of landmarks. Landmarks are points which are easy recognisable locations on the face such as the eyes, nose and mouth. \ud \ud A probabilistic, maximum a posteriori approach to finding landmarks in a facial image is proposed, which provides a theoretical framework for template based landmarkers. One such landmarker, based on a likelihood ratio detector, is discussed in detail. Special attention is paid to training and implementation issues, in order to minimize storage and processing requirements. In particular, a fast approximate singular value decomposition method is proposed to speed up the training process and an implementation of the landmarker in the Fourier domain is presented that will speed up the search process. A subspace method for outlier correction and an alternative implementation of the landmarker are shown to improve its accuracy. The impact of carefully tuning the many parameters of the method is shown. The method is extensively tested and compared with alternatives.\ud \ud Although state of the art face recognition still has a giant leap to make, before it is as good as on television, small steps are made by men all the time. \ud \u

RECENT ADVANCEMENTS IN EAR BIOMETRICS: A REVIEW

Ascertaining the identity of a person is quite an important aspect of Forensic Science. There are so many physiological features have been proved to be highly discriminating among individuals. Biometrics play a significant role in individualizing a person. Fingerprint, Palm print, Retina and Iris recognition are the most popular examples of it. Fingerprint and iris are generally considered to allow more accurate biometric recognition than the face, but the face is more easily used in surveillance scenarios where fingerprint and iris capture are not feasible. However, the face by itself is not yet as accurate and flexible as desired for this scenario due to expression changes, source of illumination, make-up, etc. Besides these limitations, ear images can be acquired in a similar manner to face images. A number of researchers have suggested that the human ear is unique enough to each individual to allow practical use as a biometric. In this article an attempt has been made to review all the recent researches of a decade made in the field of Ear Biometrics

NEURAL NETWORK BASED AGE CLASSIFICATION USING LINEAR WAVELET TRANSFORMS

The facial image analysis for classifying human age has a vital role in Image processing, Pattern recognition, Computer vision, Cognitive science and Forensic science. The various computational and mathematical models, for classifying facial age includes Principal Component Analysis (PCA) and Wavelet Transforms and Local Binary Pattern (LBP). A more sophisticated method is introduced to improve the performance of the system by decomposing the face image using 2-level linear wavelet transforms and classifying the human age group using Artificial Neural Network. This approach needs normalizing the facial image at first and then extracting the face features using linear wavelet transforms. The distance of the features is measured using Euclidean distance and given as input to Adaptive Resonance Theory (ART). The network is trained with an own dataset consisting of 70 facial images of various age group. The goal of the proposed work is to classify the human age group into four categories as Child, Adolescence, Adult and Senior Adult

Forensic Face Recognition: A Survey

Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework

Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems

This is the author’s version of a work that was accepted for publication in Forensic Science International. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International, Vol 155, Issue 2 (20 December 2005) DOI: 10.1016/j.forsciint.2004.11.007The Bayesian approach provides a unified and logical framework for the analysis of evidence and to provide results in the form of likelihood ratios (LR) from the forensic laboratory to court. In this contribution we want to clarify how the biometric scientist or laboratory can adapt their conventional biometric systems or technologies to work according to this Bayesian approach. Forensic systems providing their results in the form of LR will be assessed through Tippett plots, which give a clear representation of the LR-based performance both for targets (the suspect is the author/source of the test pattern) and non-targets. However, the computation procedures of the LR values, especially with biometric evidences, are still an open issue. Reliable estimation techniques showing good generalization properties for the estimation of the between- and within-source variabilities of the test pattern are required, as variance restriction techniques in the within-source density estimation to stand for the variability of the source with the course of time. Fingerprint, face and on-line signature recognition systems will be adapted to work according to this Bayesian approach showing both the likelihood ratios range in each application and the adequacy of these biometric techniques to the daily forensic work.This work has been partially supported under MCYT Projects TIC2000-1683, TIC2000-1669, TIC2003-09068, TIC2003-08382 and Spanish Police Force ‘‘Guardia Civil’’ Research Program