Image-based family verification in the wild

Facial image analysis has been an important subject of study in the communities of pat- tern recognition and computer vision. Facial images contain much information about the person they belong to: identity, age, gender, ethnicity, expression and many more. For that reason, the analysis of facial images has many applications in real world problems such as face recognition, age estimation, gender classification or facial expression recognition. Visual kinship recognition is a new research topic in the scope of facial image analysis. It is essential for many real-world applications. However, nowadays there exist only a few practical vision systems capable to handle such tasks. Hence, vision technology for kinship-based problems has not matured enough to be applied to real- world problems. This leads to a concern of unsatisfactory performance when attempted on real-world datasets. Kinship verification is to determine pairwise kin relations for a pair of given images. It can be viewed as a typical binary classification problem, i.e., a face pair is either related by kinship or it is not. Prior research works have addressed kinship types for which pre-existing datasets have provided images, annotations and a verification task protocol. Namely, father-son, father-daughter, mother-son and mother-daughter. The main objective of this Master work is the study and development of feature selection and fusion for the problem of family verification from facial images. To achieve this objective, there is a main tasks that can be addressed: perform a compara- tive study on face descriptors that include classic descriptors as well as deep descriptors. The main contributions of this Thesis work are: 1. Studying the state of the art of the problem of family verification in images. 2. Implementing and comparing several criteria that correspond to different face rep- resentations (Local Binary Patterns (LBP), Histogram Oriented Gradients (HOG), deep descriptors)

Unsupervised learning of clutter-resistant visual representations from natural videos

Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning rules are not known, recent results [4, 5, 6] suggest the operation of an unsupervised temporal-association-based method e.g., Foldiak's trace rule [7]. Such methods exploit the temporal continuity of the visual world by assuming that visual experience over short timescales will tend to have invariant identity content. Thus, by associating representations of frames from nearby times, a representation that tolerates whatever transformations occurred in the video may be achieved. Many previous studies verified that such rules can work in simple situations without background clutter, but the presence of visual clutter has remained problematic for this approach. Here we show that temporal association based on large class-specific filters (templates) avoids the problem of clutter. Our system learns in an unsupervised way from natural videos gathered from the internet, and is able to perform a difficult unconstrained face recognition task on natural images: Labeled Faces in the Wild [8]

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Deep Image Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR.Comment: 20 pages, 11 figure