Gait Analysis for Gender Classification in Forensics

Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion

Gotcha-I: A Multiview Human Videos Dataset

The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All these traits are useful for a multibiometric identification or a person re-identification in a video surveillance context. Body Worn Cameras (BWCs) are used by the police of different countries all around the word and their use is growing significantly. This raises the need to develop new recognition methods that consider multibiometric traits on person re-identification. The purpose of this work is to present a new video dataset called Gotcha-I. This dataset has been obtained using more mobile cameras to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environments to address both security and surveillance problem. During these videos, subjects may have a different behavior in videos such as freely, path, upstairs, avoid the camera. The dataset is composed by 493 videos including a set of 180° videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as: the 3D model of the face of each subject with all the poses of the head in pitch, yaw and roll; and the body keypoint coordinates of the gait for each video frame. It’s also shown an application of gender recognition performed on Gotcha-I, confirming the usefulness and innovativeness of the proposed dataset

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