Age & Gender Recognition in The Wild

L'estimació de grups demogràfics a partir d'imatges, i en particular pel que fa a l'estimació d'edat i sexe, és un sector amb un ampli ventall d'aplicacions. Tanmateix, l'estat de l'art actual està poc encarat a escenaris realistes que no contemplen cap mena de restriccions, la qual cosa fa que els seus mètodes siguin inservibles per certs tipus de dades de la vida real. Aquesta tesi analitza la qüestió de la predicció robusta per sexe i edat, i proposa un nou paradigma per construir un marc de treball alternatiu des d'on desenvolupar mètodes més capaços d'actuar en situacions realistes. En concret, es demostra empíricament com l'estat de l'art basat en trets facials no és capaç d'actuar al nostre conjunt de dades que representen aquestes situacions realistes, i presentem un mètode basat en Xarxes Neuronals Profundes (DNNs, per la seva abreviació en anglès) que actua com un model de predicció conjunta, incloent-hi prediccions fetes a partir de característiques extretes de tot el cos a més a més de les aconseguides a través del rostre. Això permet al model actuar quan les cares són poc visibles o estan obstruïdes, i aprofitar-se de la informació addicional quan aquestes són visibles. El sistema presentat combina diversos models aplicats en fred, com per exemple RetinaFace i ShuffleNet per tasques facials, i una Faster R-CNN pre-entrenada amb COCO amb una ResNet com a model vertebral per detecció humana. Per la meva part, també s'ha entrenat un mòdul per predir sexe i edat a partir de deteccions corporals, on es fa servir EfficientNet com a vertebral. Consegüentment, s'ha demostrat que els models basats en cos tenen la capacitat de ser més resilients.The estimation of demographics from image data, and in particular of gender and age, is a subject with an extensive amount of applications. However, current state-of-the-art is not entirely focused on realistic and unconstrained scenarios, which makes those approaches unusable for certain real-life settings. This thesis analyzes the issue of robust age and gender prediction, and proposes a new paradigm to build upon an alternative framework from which methods that are more capable in realistic situations can be developed. Namely, we present a method based on Deep Neural Networks (DNNs) that acts as an ensemble model, including predictions from both corporal and facial features. Thus, our model can act both when faces are not very visible or are occluded, and can take advantage of the extra information when they are visible. The system presented combines multiple off-the-shelf models such as RetinaFace and ShuffleNet for facial tasks, and Faster R-CNN with ResNet backbone pre-trained on COCO for human detection. From my side, a module was trained to predict gender and age based on body detections, where EfficientNet is used as backbone. Consequently, it was demonstrated that body-based models have the capacity to be more resilient

Face recognition in the wild.

Research in face recognition deals with problems related to Age, Pose, Illumination and Expression (A-PIE), and seeks approaches that are invariant to these factors. Video images add a temporal aspect to the image acquisition process. Another degree of complexity, above and beyond A-PIE recognition, occurs when multiple pieces of information are known about people, which may be distorted, partially occluded, or disguised, and when the imaging conditions are totally unorthodox! A-PIE recognition in these circumstances becomes really “wild” and therefore, Face Recognition in the Wild has emerged as a field of research in the past few years. Its main purpose is to challenge constrained approaches of automatic face recognition, emulating some of the virtues of the Human Visual System (HVS) which is very tolerant to age, occlusion and distortions in the imaging process. HVS also integrates information about individuals and adds contexts together to recognize people within an activity or behavior. Machine vision has a very long road to emulate HVS, but face recognition in the wild, using the computer, is a road to perform face recognition in that path. In this thesis, Face Recognition in the Wild is defined as unconstrained face recognition under A-PIE+; the (+) connotes any alterations to the design scenario of the face recognition system. This thesis evaluates the Biometric Optical Surveillance System (BOSS) developed at the CVIP Lab, using low resolution imaging sensors. Specifically, the thesis tests the BOSS using cell phone cameras, and examines the potential of facial biometrics on smart portable devices like iPhone, iPads, and Tablets. For quantitative evaluation, the thesis focused on a specific testing scenario of BOSS software using iPhone 4 cell phones and a laptop. Testing was carried out indoor, at the CVIP Lab, using 21 subjects at distances of 5, 10 and 15 feet, with three poses, two expressions and two illumination levels. The three steps (detection, representation and matching) of the BOSS system were tested in this imaging scenario. False positives in facial detection increased with distances and with pose angles above ± 15°. The overall identification rate (face detection at confidence levels above 80%) also degraded with distances, pose, and expressions. The indoor lighting added challenges also, by inducing shadows which affected the image quality and the overall performance of the system. While this limited number of subjects and somewhat constrained imaging environment does not fully support a “wild” imaging scenario, it did provide a deep insight on the issues with automatic face recognition. The recognition rate curves demonstrate the limits of low-resolution cameras for face recognition at a distance (FRAD), yet it also provides a plausible defense for possible A-PIE face recognition on portable devices

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components -- identity- and age-related features -- in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. Code is available at http://hzzone.github.io/MTLFace.Comment: TPAMI 2022. arXiv admin note: substantial text overlap with arXiv:2103.0152

Antibody responses to avian influenza viruses in wild birds broaden with age

For viruses such as avian influenza, immunity within a host population can drive the emergence of new strains by selecting for viruses with novel antigens that avoid immune recognition. The accumulation of acquired immunity with age is hypothesized to affect how influenza viruses emerge and spread in species of different lifespans. Despite its importance for understanding the behaviour of avian influenza viruses, little is known about age-related accumulation of immunity in the virus's primary reservoir, wild birds. To address this, we studied the age structure of immune responses to avian influenza virus in a wild swan population (Cygnus olor), before and after the population experienced an outbreak of highly pathogenic H5N1 avian influenza in 2008. We performed haemagglutination inhibition assays on sampled sera for five avian influenza strains and show that breadth of response accumulates with age. The observed age-related distribution of antibody responses to avian influenza strains may explain the age-dependent mortality observed during the highly pathogenic H5N1 outbreak. Age structures and species lifespan are probably important determinants of viral epidemiology and virulence in birds