Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Gait analysis of smart phones with the help of the accelerometer sensor

Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.The ability to measure human movements in sports fields is among the important issues for performance measurement and development. This instance is also an important part of clinical evaluations. Electromagnetic systems are among the most widely used methods to evaluate human movements. In this study, walking data of 50 different people were used in a 100-meter-long corridor. The walking dataset was obtained from the accelerometer sensor with a software developed for the smartphone. Three-dimensional Local Binary Pattern (LBP) method was applied to the dataset and a total of 768 features were generated. Datasets were made with different classification algorithms and 97.2% successful classification was achieved with Subspace KNN. In the classification according to gender, 99.7% successful classification was obtained. With this method, it is thought that more economical methods will be developed instead of high-cost devices in detecting gait disorders

Automatic real and apparent age estimation in still images

We performed a study on age estimation via still images creating a new face image database containing real age and apparent age label annotations. Two age estimation methods are proposed using the state of the art techniques and analyse their performance with the proposed database

Реализация методов классификации людей по полу и возрасту и их повторной идентификации в видеопотоке с помощью технологий глубокого обучения

В настоящее время всё большей популярностью пользуются интеллектуальные системы видеонаблюдения, способные автоматизировать аналитику отслеживаемых объектов. В работе предложены алгоритмы классификации людей по полу и возрасту, а также создания анонимных индивидуальных отпечатков объектов по графическим признакам для обеспечения повторного распознавания. Алгоритмы основаны на использовании методов глубокого обучения на наборах ограничивающих окон треклетов объектов.Currently, intelligent video surveillance systems capable of automating the analytics of tracked objects are becoming increasingly popular. The paper proposes algorithms for classifying people by gender and age, as well as creating anonymous individual prints of objects based on graphic features to ensure repeated recognition. The algorithms are based on the use of deep learning methods on sets of bounding windows of object tracklets

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

Extending quality and covariate analyses for gait biometrics

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependent covariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of the database evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments