Human identification using facial comparative descriptions

Reid, Daniel A.; Nixon, Mark S.

doi:10.1109/icb.2013.6612962

Cited by 26 publications

(46 citation statements)

References 15 publications

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“…6. In this figure (left) the continuous features show that for MORPH DB the nose (21), forehead width (2), and chin height (27) are the most stable features, due mainly to the normalization process. Face images are normalized based on the distance between the eye centres therefore the real horizontal width of some traits is lost.…”

Section: Stability Analysismentioning

confidence: 99%

“…This means that for example when the eyebrows separation increases, their length decreases. Note an important inverse correlation between chin width (26) and height (27) in MORPH DB that denotes a singularity in this population analysed.…”

Section: Correlation Between Featuresmentioning

confidence: 99%

“…As shown in Table 2 in boldface, the most frequently selected continuous features across the databases are: eyebrows separation (3), nose width (19), nose height (20), and chin height (27), which correspond to some of the most discriminative features individually (see Table 4: Fusion results of the best systems in Fig. 9 and SFFS results in Tables 2 and 3 for the continuous (s Mahalanobis ) and discrete (s Hamming ) features for ATVS Forensic and MORPH DBs.…”

Section: Analysis Of Feature Selection: Sffsmentioning

confidence: 99%

“…Other recent publications used comparative human descriptions for facial identification, e.g. [27], where twenty-seven comparative traits were used to accurately describe facial features. The latest related work published by Klare et al [28] presented a method for using human describable face attributes to perform face identification in criminal investigations.…”

Section: Introductionmentioning

confidence: 99%

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Facial soft biometric features for forensic face recognition

Tomé

Vera-Rodríguez

Fiérrez

et al. 2015

Forensic Science International

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis 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 analysed 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.

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Section: Stability Analysismentioning

confidence: 99%

Section: Correlation Between Featuresmentioning

confidence: 99%

Section: Analysis Of Feature Selection: Sffsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facial soft biometric features for forensic face recognition

Tomé

Vera-Rodríguez

Fiérrez

et al. 2015

Forensic Science International

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“…Reid et al [119] produce a study of the benefits of comparative soft biometrics over their categorical contemporaries, for the purpose of soft biometric based facial recognition. In their research they explore the ability to exploit the innate human ability of comparing one subjects attributes with that of another, in an attempt to provide more reliable and robust facial features.…”

Section: Semantic Descriptor Analysismentioning

confidence: 99%

Locating People in Video Surveillance from Semantic Descriptions

Halstead¹

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AcknowledgmentsThe completion of my PhD research programme has been an enjoyable and at times difficult and complex journey that has involved a number of people that have assisted me in so many ways throughout the process. The people that I thank here is by no means a comprehensive list as there isn't enough paper for the names I have to mention.I would firstly like to thank my supervisory team, Clinton Fookes, Sridha Sridharan, and Simon Denman for providing valuable guidance throughout the course of my study. I would like to thank Clinton and Sridha for providing me with the opportunity to study my PhD, and also for providing the Vision and Signal Processing (VSP) laboratory environment that allowed me to research at a high level, and interact with other PhD students to assist with my PhD. I would like to make special mention of Simon for his day to day support and being consistently available to help me with my endless questions, and without his help, guidance, and encouragement I would not have made it to the end of my PhD. Another member of the of the VSP discipline that I would like to thank is David Dean, as a member of VSP he assisted me multiple times throughout the course of my PhD.I would also like to thank the other students and past and present staff within the VSP laboratory, thank you for the sharing of knowledge but mostly thank you all for the entertainment, and enjoyable moments that we shared throughout this time. AbstractIn surveillance and security situations it is often necessary to search and locate a subject of interest from a verbally or textually supplied subject description. At present, searches using these forms of queries are still predominantly performed manually and often ineffectively, via physical searching of a premises or personally watching countless hours of video footage, looking for the specific subject of interest. Surveillance networks are typically monitored by few people viewing several monitors displaying single and multiple camera feeds. In these situations, the ability to translate an externally supplied description into one of computer vision relevance would be of great assistance.To date, state-of-the-art soft biometric based search techniques primarily include some form of pre-enrollment component, where the target subject is enrolled from one camera and searched in other cameras for a match, or alternatively enrolled in an earlier instance of the same camera, and searched for in later footage. These re-identification techniques exclude however, instances where the subject has not previously been located and enrolled from the footage.Soft biometric based semantic search is one technique that provides a solution to this problem. The target query no longer requires scene interrogation and instead can be generated from a textually supplied query. While soft biometrics have obvious limitations, non-permanence and not individually identifiable, they are generally simplistic for a human operator to visually extract and relay to another human operator. They also have th...

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Clothing Analysis for Subject Identification and Retrieval

Jaha

Nixon

2017

Intelligent Systems Reference Library

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Human identification using facial comparative descriptions

Cited by 26 publications

References 15 publications

Facial soft biometric features for forensic face recognition

Facial soft biometric features for forensic face recognition

Locating People in Video Surveillance from Semantic Descriptions

Clothing Analysis for Subject Identification and Retrieval

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