A system for modeling social traits in realistic faces with artificial intelligence

Los seres humanos han desarrollado especialmente su capacidad perceptiva para procesar caras y extraer información de las características faciales. Usando nuestra capacidad conductual para percibir rostros, hacemos atribuciones tales como personalidad, inteligencia o confiabilidad basadas en la apariencia facial que a menudo tienen un fuerte impacto en el comportamiento social en diferentes dominios. Por lo tanto, las caras desempeñan un papel fundamental en nuestras relaciones con otras personas y en nuestras decisiones cotidianas. Con la popularización de Internet, las personas participan en muchos tipos de interacciones virtuales, desde experiencias sociales, como juegos, citas o comunidades, hasta actividades profesionales, como e-commerce, e-learning, e-therapy o e-health. Estas interacciones virtuales manifiestan la necesidad de caras que representen a las personas reales que interactúan en el mundo digital: así surgió el concepto de avatar. Los avatares se utilizan para representar a los usuarios en diferentes escenarios y ámbitos, desde la vida personal hasta situaciones profesionales. En todos estos casos, la aparición del avatar puede tener un efecto no solo en la opinión y percepción de otra persona, sino en la autopercepción, que influye en la actitud y el comportamiento del sujeto. De hecho, los avatares a menudo se emplean para obtener impresiones o emociones a través de expresiones no verbales, y pueden mejorar las interacciones en línea o incluso son útiles para fines educativos o terapéuticos. Por lo tanto, la posibilidad de generar avatares de aspecto realista que provoquen un determinado conjunto de impresiones sociales supone una herramienta muy interesante y novedosa, útil en un amplio abanico de campos. Esta tesis propone un método novedoso para generar caras de aspecto realistas con un perfil social asociado que comprende 15 impresiones diferentes. Para este propósito, se completaron varios objetivos parciales. En primer lugar, las características faciales se extrajeron de una base de datos de caras reales y se agruparon por aspecto de una manera automática y objetiva empleando técnicas de reducción de dimensionalidad y agrupamiento. Esto produjo una taxonomía que permite codificar de manera sistemática y objetiva las caras de acuerdo con los grupos obtenidos previamente. Además, el uso del método propuesto no se limita a las características faciales, y se podría extender su uso para agrupar automáticamente cualquier otro tipo de imágenes por apariencia. En segundo lugar, se encontraron las relaciones existentes entre las diferentes características faciales y las impresiones sociales. Esto ayuda a saber en qué medida una determinada característica facial influye en la percepción de una determinada impresión social, lo que permite centrarse en la característica o características más importantes al diseñar rostros con una percepción social deseada. En tercer lugar, se implementó un método de edición de imágenes para generar una cara totalmente nueva y realista a partir de una definición de rostro utilizando la taxonomía de rasgos faciales antes mencionada. Finalmente, se desarrolló un sistema para generar caras realistas con un perfil de rasgo social asociado, lo cual cumple el objetivo principal de la presente tesis. La principal novedad de este trabajo reside en la capacidad de trabajar con varias dimensiones de rasgos a la vez en caras realistas. Por lo tanto, en contraste con los trabajos anteriores que usan imágenes con ruido, o caras de dibujos animados o sintéticas, el sistema desarrollado en esta tesis permite generar caras de aspecto realista eligiendo los niveles deseados de quince impresiones: Miedo, Enfado, Atractivo, Cara de niño, Disgustado, Dominante, Femenino, Feliz, Masculino, Prototípico, Triste, Sorprendido, Amenazante, Confiable e Inusual. Los prometedores resultados obtenidos permitirán investigar más a fondo cómo modelar lHumans have specially developed their perceptual capacity to process faces and to extract information from facial features. Using our behavioral capacity to perceive faces, we make attributions such as personality, intelligence or trustworthiness based on facial appearance that often have a strong impact on social behavior in different domains. Therefore, faces play a central role in our relationships with other people and in our everyday decisions. With the popularization of the Internet, people participate in many kinds of virtual interactions, from social experiences, such as games, dating or communities, to professional activities, such as e-commerce, e-learning, e-therapy or e-health. These virtual interactions manifest the need for faces that represent the actual people interacting in the digital world: thus the concept of avatar emerged. Avatars are used to represent users in different scenarios and scopes, from personal life to professional situations. In all these cases, the appearance of the avatar may have an effect not only on other person's opinion and perception but on self-perception, influencing the subject's own attitude and behavior. In fact, avatars are often employed to elicit impressions or emotions through non-verbal expressions, and are able to improve online interactions or even useful for education purposes or therapy. Then, being able to generate realistic looking avatars which elicit a certain set of desired social impressions poses a very interesting and novel tool, useful in a wide range of fields. This thesis proposes a novel method for generating realistic looking faces with an associated social profile comprising 15 different impressions. For this purpose, several partial objectives were accomplished. First, facial features were extracted from a database of real faces and grouped by appearance in an automatic and objective manner employing dimensionality reduction and clustering techniques. This yielded a taxonomy which allows to systematically and objectively codify faces according to the previously obtained clusters. Furthermore, the use of the proposed method is not restricted to facial features, and it should be possible to extend its use to automatically group any other kind of images by appearance. Second, the existing relationships among the different facial features and the social impressions were found. This helps to know how much a certain facial feature influences the perception of a given social impression, allowing to focus on the most important feature or features when designing faces with a sought social perception. Third, an image editing method was implemented to generate a completely new, realistic face from just a face definition using the aforementioned facial feature taxonomy. Finally, a system to generate realistic faces with an associated social trait profile was developed, which fulfills the main objective of the present thesis. The main novelty of this work resides in the ability to work with several trait dimensions at a time on realistic faces. Thus, in contrast with the previous works that use noisy images, or cartoon-like or synthetic faces, the system developed in this thesis allows to generate realistic looking faces choosing the desired levels of fifteen impressions, namely Afraid, Angry, Attractive, Babyface, Disgusted, Dominant, Feminine, Happy, Masculine, Prototypical, Sad, Surprised, Threatening, Trustworthy and Unusual. The promising results obtained in this thesis will allow to further investigate how to model social perception in faces using a completely new approach.Els sers humans han desenvolupat especialment la seua capacitat perceptiva per a processar cares i extraure informació de les característiques facials. Usant la nostra capacitat conductual per a percebre rostres, fem atribucions com ara personalitat, intel·ligència o confiabilitat basades en l'aparença facial que sovint tenen un fort impacte en el comportament social en diferents dominis. Per tant, les cares exercixen un paper fonamental en les nostres relacions amb altres persones i en les nostres decisions quotidianes. Amb la popularització d'Internet, les persones participen en molts tipus d'inter- accions virtuals, des d'experiències socials, com a jocs, cites o comunitats, fins a activitats professionals, com e-commerce, e-learning, e-therapy o e-health. Estes interaccions virtuals manifesten la necessitat de cares que representen a les persones reals que interactuen en el món digital: així va sorgir el concepte d'avatar. Els avatars s'utilitzen per a representar als usuaris en diferents escenaris i àmbits, des de la vida personal fins a situacions professionals. En tots estos casos, l'aparició de l'avatar pot tindre un efecte no sols en l'opinió i percepció d'una altra persona, sinó en l'autopercepció, que influïx en l'actitud i el comportament del subjecte. De fet, els avatars sovint s'empren per a obtindre impressions o emocions a través d'expressions no verbals, i poden millorar les interaccions en línia o inclús són útils per a fins educatius o terapèutics. Per tant, la possibilitat de generar avatars d'aspecte realista que provoquen un determinat conjunt d'impressions socials planteja una ferramenta molt interessant i nova, útil en un ampla varietat de camps. Esta tesi proposa un mètode nou per a generar cares d'aspecte realistes amb un perfil social associat que comprén 15 impressions diferents. Per a este propòsit, es van completar diversos objectius parcials. En primer lloc, les característiques facials es van extraure d'una base de dades de cares reals i es van agrupar per aspecte d'una manera automàtica i objectiva emprant tècniques de reducció de dimensionalidad i agrupament. Açò va produir una taxonomia que permet codificar de manera sistemàtica i objectiva les cares d'acord amb els grups obtinguts prèviament. A més, l'ús del mètode proposat no es limita a les característiques facials, i es podria estendre el seu ús per a agrupar automàticament qualsevol altre tipus d'imatges per aparença. En segon lloc, es van trobar les relacions existents entre les diferents característiques facials i les impressions socials. Açò ajuda a saber en quina mesura una determinada característica facial influïx en la percepció d'una determinada impressió social, la qual cosa permet centrar-se en la característica o característiques més importants al dissenyar rostres amb una percepció social desitjada. En tercer lloc, es va implementar un mètode d'edició d'imatges per a generar una cara totalment nova i realista a partir d'una definició de rostre utilitzant la taxonomia de trets facials abans mencionada. Finalment, es va desenrotllar un sistema per a generar cares realistes amb un perfil de tret social associat, la qual cosa complix l'objectiu principal de la present tesi. La principal novetat d'este treball residix en la capacitat de treballar amb diverses dimensions de trets al mateix temps en cares realistes. Per tant, en contrast amb els treballs anteriors que usen imatges amb soroll, o cares de dibuixos animats o sintètiques, el sistema desenrotllat en esta tesi permet generar cares d'aspecte realista triant els nivells desitjats de quinze impressions: Por, Enuig, Atractiu, Cara de xiquet, Disgustat, Dominant, Femení, Feliç, Masculí, Prototípic, Trist, Sorprés, Amenaçador, Confiable i Inusual. Els prometedors resultats obtinguts en esta tesi permetran investigar més a fons com modelar la percepció social en les cares utilitzant un enfocament completFuentes Hurtado, FJ. (2018). A system for modeling social traits in realistic faces with artificial intelligence [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/101943TESI

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Design and analysis of a content-based image retrieval system

The automatic retrieval of images according to the similarity of their content is a challenging task with many application fields. In this book the automatic retrieval of images according to human spontaneous perception without further effort or knowledge is considered. A system is therefore designed and analyzed. Methods for the detection and extraction of regions and for the extraction and comparison of color, shape, and texture features are also investigated

Perception and recognition of computer-enhanced facial attributes and abstracted prototypes

The influence of the human facial image was surveyed and the nature of its many interpretations were examined. The role of distinctiveness was considered particularly relevant as it accounted for many of the impressions of character and identity ascribed to individuals. The notion of structural differences with respect to some selective essence of normality is especially important as it allows a wide range of complex facial types to be considered and understood in an objective manner. A software tool was developed which permitted the manipulation of facial images. Quantitative distortions of digital images were examined using perceptual and recognition memory paradigms. Seven experiments investigated the role of distinctiveness in memory for faces using synthesised caricatures. The results showed that caricatures, both photographic and line-drawing, improved recognition speed and accuracy, indicating that both veridical and distinctiveness information are coded for familiar faces in long-term memory. The impact of feature metrics on perceptual estimates of facial age was examined using 'age-caricatured' images and were found to be in relative accordance with the 'intended' computed age. Further modifying the semantics permitted the differences between individual faces to be visualised in terms of facial structure and skin texture patterns. Transformations of identity between two, or more, faces established the necessary matrices which can offer an understanding of facial expression in a categorical manner and the inherent interactions. A procedural extension allowed generation of composite images in which all features are perfectly aligned. Prototypical facial types specified in this manner enabled high-level manipulations to be made of gender and attractiveness; two experiments corroborated previously speculative material and thus gave credence to the prototype model. In summary, psychological assessment of computer-manipulated facial images demonstrated the validity of the objective techniques and highlighted particular parameters which contribute to our perception and recognition of the individual and of underlying facial types

Material perception and action : The role of material properties in object handling

This dissertation is about visual perception of material properties and their role in preparation for object handling. Usually before an object is touched or picked-up we estimate its size and shape based on visual features to plan the grip size of our hand. After we have touched the object, the grip size is adjusted according to the provided haptic feedback and the object is handled safely. Similarly, we anticipate the required grip force to handle the object without slippage, based on its visual features and prior experience with similar objects. Previous studies on object handling have mostly examined object characteristics that are typical for object recognition, e.g., size, shape, weight, but in the recent years there has been a growing interest in object characteristics that are more typical to the type of material the object is made from. That said, in a series of studies we investigated the role of perceived material properties in decision-making and object handling, in which both digitally rendered materials and real objects made of different types of materials were presented to human subjects and a humanoid robot. Paper I is a reach-to-grasp study where human subjects were examined using motion capture technology. In this study, participants grasped and lifted paper cups that varied in appearance (i.e., matte vs. glossy) and weight. Here we were interested in both the temporal and spatial components of prehension to examine the role of material properties in grip preparation, and how visual features contribute to inferred hardness before haptic feedback has become available. We found the temporal and spatial components were not exclusively governed by the expected weight of the paper cups, instead glossiness and expected hardness has a significant role as well. In paper II, which is a follow-up on Paper I, we investigated the grip force component of prehension using the same experimental stimuli as used in paper I. In a similar experimental set up, using force sensors we examined the early grip force magnitudes applied by human subjects when grasping and lifting the same paper cups as used in Paper I. Here we found that early grip force scaling was not only guided by the object weight, but the visual characteristics of the material (i.e., matte vs. glossy) had a role as well. Moreover, the results suggest that grip force scaling during the initial object lifts is guided by expected hardness that is to some extend based on visual material properties. Paper III is a visual judgment task where psychophysical measurements were used to examine how the material properties, roughness and glossiness, influence perceived bounce height and consequently perceived hardness. In a paired-comparison task, human subjects observed a bouncing ball bounce on various surface planes and judged their bounce height. Here we investigated, what combination of surface properties, i.e., roughness or glossiness, makes a surface plane to be perceived bounceable. The results demonstrate that surface planes with rough properties are believed to afford higher bounce heights for the bouncing ball, compared to surface planes with smooth properties. Interestingly, adding shiny properties to the rough and smooth surface planes, reduced the judged difference, as if surface planes with gloss are believed to afford higher bounce heights irrespective of how smooth or rough the surface plane is beneath. This suggests that perceived bounce height involves not only the physical elements of the bounce height, but also the visual characteristics of the material properties of the surface planes the ball bounces on. In paper IV we investigated the development of material knowledge using a robotic system. A humanoid robot explored real objects made of different types of materials, using both camera and haptic systems. The objects varied in visual appearances (e.g., texture, color, shape, size), weight, and hardness, and in two experiments, the robot picked up and placed the experimental objects several times using its arm. Here we used the haptic signals from the servos controlling the arm and the shoulder of the robot, to obtain measurements of the weight and hardness of the objects, and the camera system to collect data on the visual features of the objects. After the robot had repeatedly explored the objects, an associative learning model was created based on the training data to demonstrate how the robotic system could produce multi-modal mapping between the visual and haptic features of the objects. In sum, in this thesis we show that visual material properties and prior knowledge of how materials look like and behave like has a significant role in action planning

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition