Individualised model of facial age synthesis based on constrained regression

YesFaces convey much information. Interestingly we humans have a remarkable ability of identifying, extracting, and interpreting this information. Recently automatic facial ageing (AFA) has gained popularity due to its numerous applications which include search for missing people, biometrics, and multimedia. The problem of AFA is faced with various challenges, including incomplete training datasets, unrestrained environments, ethnic and gender variations to mention but a few. This work presents a new approach to automatic facial ageing which involves the development of a person specific facial ageing system. A color based Active Appearance Model (AAM) is used to extract facial features. Then, regression is used to model an age estimator. Age synthesis is achieved by computing a solution that minimises the distance from the original face with the use of constrained regression. The model is tested on a challenging database of single image per person. Initial results suggest that plausible images can be rerendered at different ages, automatically using the AAM representation. Using the constrained regressor we are guaranteed to get estimated ages that are exact for an individual at a given age

Gender and age classification based on facial features

El análisis facial es uno de los procesos claves para las interacciones humanas en la vida diaria. La gente es capaz de inferir el género, la edad, las emociones, etc. desde una única imagen de un rostro humano. También son capaces de reconocer con precisión a las personas que han conocido antes a pesar de los cambios en la iluminación, oclusiones parciales o cambios de perspectiva. Un sistema capaz de detectar automáticamente los rostros y extraer datos de estas caras, sería muy útil para muchas aplicaciones diferentes: la seguridad, la vigilancia, la publicidad personalizada, las interfaces hombre-máquina, etc. Este trabajo describe un sistema de clasificación de género y edad basado en el análisis facial. El sistema es capaz de detectar automáticamente y procesar rostros humanos para extraer estos datos, utilizando las siguientes etapas: - La detección de rostros humanos en el entorno. Sólo los rostros que estén mirando a la cámara serán considerados para la clasificación, aunque se permite un cierto grado de cambio de perspectiva. - Los rostros detectados son normalizados a un tamaño y perspectiva estándar. - Los rostros normalizados son caracterizados utilizando sus vectores LBP. - Seis SVM entrenados previamente serán utilizados para determinar el género y el rango de edad de la persona empleando el LBP. El sistema ha sido probado usando un dispositivo Microsoft Kinect para capturar imágenes de entrada, y la Microsoft Kinect SDK para detectar y rastrear rostros. La biblioteca OpenCV también se ha empleado para normalizar las imágenes de la cara. Los seis clasificadores SVM han sido entrenados usando imágenes de la cara de la base de datos FERET. Por último, todo el sistema se ha integrado en RoboComp, un framework que utiliza Ice para comunicar los diferentes componentes del software. Los resultados muestran que el sistema es capaz de clasificar correctamente el 81% de las personas detectadas, a una velocidad de 12 imágenes por segundo en un PC estándar y usando secuencias de vídeo sin restricciones como entrad

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Development of an Illumination Invariant Face Recognition System

The Face recognition systems have gained much attention for applications in surveillance, access control, forensics, border control. Face recognition systems encounter challenges due to variation in illumination, pose, expression, occlusion and most importantly, aging. The effect of the intensity of light on recognition image in contract with gallery image significantly affect the face recognition system. In this study, an illumination invariant Face Recognition System is developed using a 4-layered Convolutional Neural Network (CNN). The proposed system was able to recognize the different degree of face Illuminated image, thus making the model Illumination Invariant Face Recognition system. The variations caused by illumination was modelled as a form of light varying noise, and it was validated by computing its error statistics and comparing its performance with existing models found in literature. The result of the study showed that an adaptive and robust face recognition system that is illumination invariant could be achieved with CNN. The recognition accuracy achieved by the study was 99.22% with five (5) epochs and iteration of 85

The Integrated Usage of LBP and HOG Transformations and Machine Learning Algorithms for Age Range Prediction from Facial Images

Age prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images’ size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%

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

What else does your biometric data reveal? A survey on soft biometrics

International audienceRecent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka