Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition

We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obtained recall rate almost doubles the best reported result to date. We discuss the various components and innovations of our system that enable this significant performance gap. These components include extensive utilization of an accurate 3D reconstructed shape model dealing with challenges arising from pose and illumination. In addition, discriminative models based on billions of faces are used in order to overcome aging and facial expression as well as low light and overexposure. Finally, we identify a challenging set of identification queries that might provide useful focus for future research.Comment: 7 page

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

4D Unconstrained Real-time Face Recognition Using a Commodity Depthh Camera

Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition. This article accompanies the reader through a succinct survey of the current literature on face recognition in general and 3D face recognition using depth sensors in particular. Consequent to the assessment of experiments performed using implementations of the most established algorithms, it can be concluded that the majority are biased towards qualitative performance and are lacking in speed. A novel method which uses noisy data from such a commodity sensor to build dynamic internal representations of faces is proposed. Distances to a surface normal to the face are measured in real-time and used as input to a specific type of recurrent neural network, namely long short-term memory. This enables the prediction of facial structure in linear time and also increases robustness towards partial occlusions

Implementing a Wizard of Oz Tool for Augmented Reality

This thesis aims to explore Wizard of Oz testing in conjunction with Augmented Reality (AR) and focus has been put on testing AR with Head Mounted Displays. The recent increase of interest in HMDs with products such as MOD Live from Recon Instruments and Google's Project Glass puts new demands and possibilities on human-computer interaction. Since the commercial market for HMDs is still in its infancy the need to explore different design approaches is very much present. One way to conduct experiments on human-machine interaction is with the help of a Wizard of Oz tool. During the thesis we have developed such a tool to support designers in researching usability and interaction. The tool provides a user friendly framework to carry out user case studies focused on AR with HMDs. After input and feedback from stakeholders and experts we believe that, even though the tool is mainly meant to be used in conjunction with AR in HMDs, the tool can be applied to other areas as well

Descarga de computación de dispositivos móviles a ambientes Cloud Computing en un caso en concreto, el reconocimiento facial

[ES] Beneficios en la descarga de computación con una aplicación de reconocimiento facial para dispositivos móviles hacia un entorno Cloud Computing con el Reconocimiento como Servicio. Se mide prestaciones en ambos plataformas y se busca un escenario colaborativo que ofresca un equilibrio entre los procesos inmersos en este contexto.[EN] Benefits in computation offloading with a facial recognition application for mobile devices to Cloud Computing environment with the Recognition as a Service. The performance is measured on both platforms and we looking a collaborative setting to permit a balance between the processes involved in this contextLuzuriaga Quichimbo, JE. (2012). Descarga de computación de dispositivos móviles a ambientes Cloud Computing en un caso en concreto, el reconocimiento facial. http://hdl.handle.net/10251/17873Archivo delegad

Photographic Mediation as a Mode of Production: Investigating the Agency of Commercial Institutions in Contemporary Vernacular Photography

This dissertation argues that to understand what is at stake in contemporary vernacular photography, it is vital to account for the commercial imperatives that are invested in our photographic apparatus. The vernacular is often seen as emerging from the milieu of everyday life, operating outside of institutional constraints. However, commercial institutions have always played a vital role in shaping the meaning and matter of vernacular photography, producing the extended network of devices and protocols through which photographic activity takes place. Vernacular photography should therefore be seen to encapsulate a series of complex negotiations between individual desires and commercial imperatives. Through an examination of three central case studies - Kodak, Snapchat and Ditto Labs - this thesis aims to elucidate how the productive potential of vernacular photography is instrumentalized as a means of generating value. Bringing together approaches from western Marxism with contemporary theories of networked media and photography, the argument is made that photographic mediation can be usefully framed as a mode of production. Photographic mediation, referring to the processual and material dynamics of photography, is employed to investigate the circuits of labour, value and desire that flow through our photographic apparatus. In performing this analysis, the concept of deterritorialization is applied as a way of understanding how photographic mediation has become more productive through destabilizing the boundaries between photography, subjectivity and the everyday. As photography proliferates and disperses into the rhythms and atmospheres that constitute daily life, it is increasingly imbricated into the performance and production of identities, relationships and desires. Under these circumstances, it becomes all the more vital that we recognize the role of commercial actors in shaping not only our photographic apparatus, but also our ways of being in, and relating to, the world

Brain inspired approach to computational face recognition

Face recognition that is invariant to pose and illumination is a problem solved effortlessly by the human brain, but the computational details that underlie such efficient recognition are still far from clear. This thesis draws on research from psychology and neuroscience about face and object recognition and the visual system in order to develop a novel computational method for face detection, feature selection and representation, and memory structure for recall. A biologically plausible framework for developing a face recognition system will be presented. This framework can be divided into four parts: 1) A face detection system. This is an improved version of a biologically inspired feedforward neural network that has modifiable connections and reflects the hierarchical and elastic structure of the visual system. The face detection system can detect if a face is present in an input image, and determine the region which contains that face. The system is also capable of detecting the pose of the face. 2) A face region selection mechanism. This mechanism is used to determine the Gabor-style features corresponding to the detected face, i.e., the features from the region of interest. This region of interest is selected using a feedback mechanism that connects the higher level layer of the feedforward neural network where ultimately the face is detected to an intermediate level where the Gabor style features are detected. 3) A face recognition system which is based on the binary encoding of the Gabor style features selected to represent a face. Two alternative coding schemes are presented, using 2 and 4 bits to represent a winning orientation at each location. The effectiveness of the Gabor-style features and the different coding schemes in discriminating faces from different classes is evaluated using the Yale B Face Database. The results from this evaluation show that this representation is close to other results on the same database. 4) A theoretical approach for a memory system capable of memorising sequences of poses. A basic network for memorisation and recall of sequences of labels have been implemented, and from this it is extrapolated a memory model that could use the ability of this model to memorise and recall sequences, to assist in the recognition of faces by memorising sequences of poses. Finally, the capabilities of the detection and recognition parts of the system are demonstrated using a demo application that can learn and recognise faces from a webcam

Face recognition in uncontrolled environments

This thesis concerns face recognition in uncontrolled environments in which the images used for training and test are collected from the real world instead of laboratories. Compared with controlled environments, images from uncontrolled environments contain more variation in pose, lighting, expression, occlusion, background, image quality, scale, and makeup. Therefore, face recognition in uncontrolled environments is much more challenging than in controlled conditions. Moreover, many real world applications require good recognition performance in uncontrolled environments. Example applications include social networking, human-computer interaction and electronic entertainment. Therefore, researchers and companies have shifted their interest from controlled environments to uncontrolled environments over the past seven years. In this thesis, we divide the history of face recognition into four stages and list the main problems and algorithms at each stage. We find that face recognition in unconstrained environments is still an unsolved problem although many face recognition algorithms have been proposed in the last decade. Existing approaches have two major limitations. First, many methods do not perform well when tested in uncontrolled databases even when all the faces are close to frontal. Second, most current algorithms cannot handle large pose variation, which has become a bottleneck for improving performance. In this thesis, we investigate Bayesian models for face recognition. Our contributions extend Probabilistic Linear Discriminant Analysis (PLDA) [Prince and Elder 2007]. In PLDA, images are described as a sum of signal and noise components. Each component is a weighted combination of basis functions. We firstly investigate the effect of degree of the localization of these basis functions and find better performance is obtained when the signal is treated more locally and the noise more globally. We call this new algorithm multi-scale PLDA and our experiments show it can handle lighting variation better than PLDA but fails for pose variation. We then analyze three existing Bayesian face recognition algorithms and combine the advantages of PLDA and the Joint Bayesian Face algorithm [Chen et al. 2012] to propose Joint PLDA. We find that our new algorithm improves performance compared to existing Bayesian face recognition algorithms. Finally, we propose Tied Joint Bayesian Face algorithm and Tied Joint PLDA to address large pose variations in the data, which drastically decreases performance in most existing face recognition algorithms. To provide sufficient training images with large pose difference, we introduce a new database called the UCL Multi-pose database. We demonstrate that our Bayesian models improve face recognition performance when the pose of the face images varies