Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning

Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.Comment: 7 page

Effect of Rosemary and Oregano Aqueous Extracts Supplementation on Microbial Growth during Refrigerated Storage of Milk

The aim of the present study is to evaluate the effect of adding rosemary and oregano aqueous extracts to raw cow milk on the dynamics of microbial growth during milk refrigeration. The aqueous extracts from plants leaves were prepared and supplemented to milk samples at different concentrations: 0, 0.25, 0.5, 0.75 and 1 mg mL-1 for rosemary and 0, 1.25, 2.5, 3.75, 5 mg mL-1 for oregano. The evolution of microbial growth was monitored at regular intervals of times during ten days of refrigerated storage at 4 °C. The enumeration of the microbial flora was conducted by the culture methods. The supplementation of the two plants extracts to refrigerated milk generated a prolongation of the lag phase duration and limited microbial growth. For rosemary, the lag phase length (λ) in milks without supplementation (0 mg mL-1) was prolonged from 1.2 ± 0.80 day (28.8 h) to 1.66 ± 0.92 day (39.84 h) in milks added with 0.75 and 1 mg mL-1 while the maximum cell load (Xmax) diminished from 7.00 ± 0.17 log CFU mL-1 in non-supplemented milks to 6.56 ± 0.19 log CFU mL-1 in milks added with the same concentrations. For oregano, λ was delayed from 4.12 ± 0.11 day (98.88 h) in milks without supplementation to 5.04 ± 0.97 day (120.96 h) in milks added with 1.25 mg mL-1. A decrease of Xmax was remarked for the whole of the concentrations, registering the lowest value of 4.45 ± 1.34 log CFU mL-1 at 2.5 and 3.75 mg mL-1. The use of rosemary and oregano aqueous extracts as natural additives during refrigeration could offer opportunities as biopreservatives in the milk industry, to reduce heat pretreatment and the addition of chemical additives

The 2013 face recognition evaluation in mobile environment

Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. © 2013 IEEE

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends

Contribution à l'analyse de visages à partir d'images RVB et de cartes de profondeur

Automatic human face analysis refers to the processing of facial images by machines in order to infer useful information, such as identity, gender, ethnicity, mood, etc. Face analysis has many interesting applications in security, human computer interaction, social media analysis, etc. Therefore, though face analysis is a well-established computer vision problem, it is still an active research topic attracting considerable attention from researchers. The research community mainly aims to develop more robust systems with the ability to fulfill the requirements of current applications.This thesis contributes to a number of face analysis tasks: face verification and identification, gender recognition, ethnicity recognition and kinship verification. Faces from three different imaging supports i.e. RGB images, depth maps and videos are used throughout the thesis. We present novel approaches and in-depth studies for solving and improving the face analysis problem.First, we tackle face verification problem from RGB images. The local binary patterns based face verification scheme has been revised through proposing novel efficient representations, which cope with the original approach drawbacks while improving the verification performance.Next, the problems of identity, gender and ethnicity recognition are investigated from both RGB and depth images. The aim is to assess the usefulness low-quality depth images, acquired with Microsoft Kinect low-cost sensor, in coping with facial analysis tasks. The performance of RGB images and depth maps are compared to show the ability of the latter ones to deal with sever environment illumination circumstances.Furthermore, the thesis contributes to the problem of kinship verification from videos, where the family relationship between two persons is checked by comparing their facial attributes. The dynamics of faces are efficiently coded by the means of spatio-temporal descriptors and deep features. The value of using videos in kinship problem is shown by comparing their performance against that of still images.Throughout the thesis, various benchmark databases are used and extensive experiments are carried out to validate our proposed approaches and developed methods.Besides, the results of the proposed approaches are compared against the state of the art, highlighting our contributions and showing improvements. Future directions for the presented contributions are outlined at end of the thesis.L'analyse automatique du visage se réfère au traitement des images faciales par les machines afin d'inférer des informations utiles, telles que l'identité, le sexe, l'ethnicité, l'humeur, etc. L'analyse du visage a de nombreuses applications intéressantes en sécurité, interaction homme-machine, analyse des médias sociaux, etc. Par conséquent, bien que l'analyse du visage soit un problème de vision en informatique bien établi, il s'agit toujours d'un sujet de recherche actif qui attire l'attention considérable des chercheurs. La communauté des sceintifiques vise principalement à développer des systèmes plus robustes avec la capacité de répondre aux exigences des applications actuelles.Cette thèse contribue à un certain nombre de tâches d'analyse faciale comme : la vérification et l'identification du visage, la reconnaissance du genre, la reconnaissance ethnique et la vérification de la parenté. Des visages à partir de trois supports d'imagerie différents, i.e. des images RVB, des cartes de profondeur et des vidéos sont utilisés tout au long de la thèse. Nous présentons de nouvelles approches et des études approfondies pour résoudre efficacement le problème de l'analyse du visage.Nous abordons en premier le problème de vérification de visage à partir d'images RVB. Le schéma de vérification du visage basé sur les modèles binaires locaux a été révisé en proposant de nouvelles représentations efficaces qui permettent de faire face aux inconvénients initiaux de l'approche tout en améliorant les performances de vérification.Ensuite, les problèmes d'identité, de sexe et d'origine ethnique sont étudiés à la fois à partir d'images RVB et de cartes de profondeur. L'objectif est d'évaluer l'utilité des images de profondeur de faible qualité, acquises avec le capteur Microsoft Kinect à faible coût, pour faire face aux tâches d'analyse faciale. Les performances des images RVB et des cartes de profondeur sont comparées pour montrer la capacité de ces dernières à faire face à des situations compliqués d'éclairement de l'environnement.En outre, la thèse contribue au problème de la vérification de parenté à partir de vidéos, où la relation de famille entre deux personnes est vérifiée en comparant leurs attributs faciaux. La dynamique des faces est efficacement codée à l'aide de descripteurs spatio-temporels et de traits profonds. L'utilisation de vidéos dans le problème de parenté est privilégiée après une comparaison des performances avec l'utilisation d'images fixes.Tout au long de la thèse, diverses bases de données de référence sont utilisées et des expériences approfondies sont effectuées pour valider les approches proposées et les méthodes développées.Par ailleurs, les résultats des approches proposées sont comparés aux méthodes de référence dans le domaine, en mettant en évidence nos contributions et en montrant des améliorations. Des orientations futures sont présentées à la fin de la thèse

Efficient Tensor-Based 2D+3D Face Verification.

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