From pixels to people: a model of familiar face recognition

Research in face recognition has largely been divided between those projects concerned with front-end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front-end is based on principal components analysis of images, and the cognitive back-end is based on a simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of "known" faces and delivers information about these people

Quantification of Facial Traits

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability

Prä- und postnatale Entwicklung topographischer Transformationen im Gehirn

This dissertation connects two independent fields of theoretical neuroscience: on the one hand, the self-organization of topographic connectivity patterns, and on the other hand, invariant object recognition, that is the recognition of objects independently of their various possible retinal representations (for example due to translations or scalings). The topographic representation is used in the presented approach, as a coordinate system, which then allows for the implementation of invariance transformations. Hence this study shows, that it is possible that the brain self-organizes before birth, so that it is able to invariantly recognize objects immediately after birth. Besides the core hypothesis that links prenatal work with object recognition, advancements in both fields themselves are also presented. In the beginning of the thesis, a novel analytically solvable probabilistic generative model for topographic maps is introduced. And at the end of the thesis, a model that integrates classical feature-based ideas with the normalization-based approach is presented. This bilinear model makes use of sparseness as well as slowness to implement "optimal" topographic representations. It is therefore a good candidate for hierarchical processing in the brain and for future research.Die vorliegende Arbeit verbindet zwei bisher unabhängig untersuchte Gebiete der theoretischen Neurowissenschaften: zum Einen die vorgeburtliche Selbstorganisation topographischer Verbindungsstrukturen und zum Anderen die invariante Objekterkennung, das heisst, die Erkennung von Objekten trotz ihrer mannigfaltigen retinalen Darstellungen (zum Beispiel durch Verschiebungen oder Skalierungen). Die topographische Repräsentierung wird hierbei während der Selbstorganisation als Koordinatensystem genutzt, um Invarianztransformationen zu implementieren. Dies zeigt die Möglichkeit auf, dass sich das Gehirn bereits vorgeburtlich detailliert selbstorganisieren kann, um nachgeburtlich sofort invariant Erkennen zu können. Im Detail führt Kapitel 2 in ein neues, probabilistisch generatives und analytisch lösbares Modell zur Ontogenese topographischer Transformationen ein. Dem Modell liegt die Annahme zugrunde, dass Ausgabezellen des Systems nicht völlig unkorreliert sind, sondern eine a priori gegebene Korrelation erreichen wollen. Da die Eingabezellen nachbarschaftskorreliert sind, hervorgerufen durch retinale Wellen, ergibt sich mit der Annahme rein erregender Verbindungen eine eindeutige topographische synaptische Verbindungsstruktur. Diese entspricht der bei vielen Spezies gefundenen topographischen Karten, z.B. der Retinotopie zwischen der Retina und dem LGN, oder zwischen dem LGN und dem Neokortex. Kapitel 3 nutzt eine abstraktere Formulierung des Retinotopiemechanismus, welche durch adiabitische Elimination der Aktivitätsvariablen erreicht wird, um den Effekt retinaler Wellen auf ein Modell höherer kortikaler Informationsverarbeitung zu untersuchen. Zu diesem Zweck wird der Kortex vereinfacht als bilineares Modell betrachtet, um einfache modulatorische Nichtlinearitäten mit in Betracht ziehen zu können. Zusätzlich zu den Ein- und Ausgabezellen kommen in diesem Modell Kontrolleinheiten zum Einsatz, welche den Informationsfluss aktiv steuern können und sich durch Wettbewerb und pränatalem Lernen auf verschiedene Muster retinaler Wellen spezialisieren. Die Ergebnisse zeigen, dass die entstehenden Verbindungsstrukturen affinen topographischen Abbildungen (insbesondere Translation, Skalierung und Orientierung) entsprechen, die nach Augenöffnen invariante Erkennung ermöglichen, da sie Objekte in der Eingabe in eine normalisierte Repräsentierung transformieren können. Das Modell wird für den eindimensionalen Fall ausführlich analysiert und die Funktionalität für den biologisch relevanteren zweidimensionalen Fall aufgezeigt. Kapitel 4 verallgemeinert das bilineare Modell des dritten Kapitels zu einem mehrschichtigen Modell, die shifter curcuits''. Diese ermöglichen eine logarithmisch in der Anzahl der Eingabezellen wachsende Anzahl an Synapsen, statt einer prohibitiv quadratischen Anzahl. Ausgenutzt wird die Orthogonalität von Translationen im Raum der Verbindungsstrukturen um diese durch harten Wettbewerb an einzelnen Synapsen zu organisieren. Neurobiologisch ist dieser Mechanismus durch Wettbewerb um einen wachstumsregulierenden Transmitter realisierbar. Kapitel 5 nutzt Methoden des probabilistischen Lernens, um das bilineare Modell auf das Lernen von optimalen Repräsentation der Eingabestatistiken zu optimieren. Da statistischen Methoden zweiter Ordnung, wie zum Beispiel das generative Modell aus Kapitel 2, keine lokalisierten rezeptiven Felder ermöglichen und somit keine (örtliche) Topographie möglich ist, wird sparseness'' verwendet um statistischen Abhängigkeiten höherer Ordnung zu lernen und gleichzeitig Topographie zu implementieren. Anwendungen des so formulierten Modells auf natürliche Bilder zeigen, dass lokalisierte, bandpass filternde rezeptive Felder entstehen, die primären kortikalen rezeptiven Feldern stark ähneln. Desweiteren entstehen durch die erzwungene Topographie Orientierungs- und Frequenzkarten, die ebenfalls kortikalen Karten ähneln. Eine Untersuchung des Modells mit zusätzlicher slowness'' der Ausgabezellen und in zeitlicher Nähe gezeigten transformierten natürlichen Eingabemustern zeigt, dass verschiedene Kontrolleinheiten konsistente und den Eingabetransformationen entsprechende rezeptive Felder entwickeln und somit invariante Darstellungen bezüglich der gezeigten Eingaben entwickeln

Improved Human Face Recognition by Introducing a New Cnn Arrangement and Hierarchical Method

Human face recognition has become one of the most attractive topics in the fields ‎of biometrics due to its wide applications. The face is a part of the body that carries ‎the most information regarding identification in human interactions. Features such ‎as the composition of facial components, skin tone, face\u27s central axis, distances ‎between eyes, and many more, alongside the other biometrics, are used ‎unconsciously by the brain to distinguish a person. Indeed, analyzing the facial ‎features could be the first method humans use to identify a person in their lives. ‎As one of the main biometric measures, human face recognition has been utilized in ‎various commercial applications over the past two decades. From banking to smart ‎advertisement and from border security to mobile applications. These are a few ‎examples that show us how far these methods have come. We can confidently say ‎that the techniques for face recognition have reached an acceptable level of ‎accuracy to be implemented in some real-life applications. However, there are other ‎applications that could benefit from improvement. Given the increasing demand ‎for the topic and the fact that nowadays, we have almost all the infrastructure that ‎we might need for our application, make face recognition an appealing topic. ‎ When we are evaluating the quality of a face recognition method, there are some ‎benchmarks that we should consider: accuracy, speed, and complexity are the main ‎parameters. Of course, we can measure other aspects of the algorithm, such as size, ‎precision, cost, etc. But eventually, every one of those parameters will contribute to ‎improving one or some of these three concepts of the method. Then again, although ‎we can see a significant level of accuracy in existing algorithms, there is still much ‎room for improvement in speed and complexity. In addition, the accuracy of the ‎mentioned methods highly depends on the properties of the face images. In other ‎words, uncontrolled situations and variables like head pose, occlusion, lighting, ‎image noise, etc., can affect the results dramatically. ‎ Human face recognition systems are used in either identification or verification. In ‎verification, the system\u27s main goal is to check if an input belongs to a pre-determined tag or a person\u27s ID. ‎Almost every face recognition system consists of four major steps. These steps are ‎pre-processing, face detection, feature extraction, and classification. Improvement ‎in each of these steps will lead to the overall enhancement of the system. In this ‎work, the main objective is to propose new, improved and enhanced methods in ‎each of those mentioned steps, evaluate the results by comparing them with other ‎existing techniques and investigate the outcome of the proposed system.

Image Understanding for Automatic Human and Machine Separation.

PhDThe research presented in this thesis aims to extend the capabilities of human interaction proofs in order to improve security in web applications and services. The research focuses on developing a more robust and efficient Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) to increase the gap between human recognition and machine recognition. Two main novel approaches are presented, each one of them targeting a different area of human and machine recognition: a character recognition test, and an image recognition test. Along with the novel approaches, a categorisation for the available CAPTCHA methods is also introduced. The character recognition CAPTCHA is based on the creation of depth perception by using shadows to represent characters. The characters are created by the imaginary shadows produced by a light source, using as a basis the gestalt principle that human beings can perceive whole forms instead of just a collection of simple lines and curves. This approach was developed in two stages: firstly, two dimensional characters, and secondly three-dimensional character models. The image recognition CAPTCHA is based on the creation of cartoons out of faces. The faces used belong to people in the entertainment business, politicians, and sportsmen. The principal basis of this approach is that face perception is a cognitive process that humans perform easily and with a high rate of success. The process involves the use of face morphing techniques to distort the faces into cartoons, allowing the resulting image to be more robust against machine recognition. Exhaustive tests on both approaches using OCR software, SIFT image recognition, and face recognition software show an improvement in human recognition rate, whilst preventing robots break through the tests