@article {357, title = {Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?}, number = {003}, year = {2014}, month = {03/2014}, abstract = {

The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be dissatisfied with it, among them is its lack of biological plausibility. A recent theory of invariant recognition by feedforward hierarchical networks, like HMAX, other convolutional networks, or possibly the ventral stream, implies an alternative approach to unconstrained face recognition. This approach accomplishes detection and alignment implicitly by storing transformations of training images (called templates) rather than explicitly detecting and aligning faces at test time. Here we propose a particular locality-sensitive hashing based voting scheme which we call {\textquotedblleft}consensus of collisions{\textquotedblright} and show that it can be used to approximate the full 3-layer hierarchy implied by the theory. The resulting end-to-end system for unconstrained face recognition operates on photographs of faces taken under natural conditions, e.g., Labeled Faces in the Wild (LFW), without aligning or cropping them, as is normally done. It achieves a drastic improvement in the state of the art on this end-to-end task, reaching the same level of performance as the best systems operating on aligned, closely cropped images (no outside training data). It also performs well on two newer datasets, similar to LFW, but more difficult: LFW-jittered (new here) and SUFR-W.

}, keywords = {Computer vision, Face recognition, Hierarchy, Invariance}, author = {Qianli Liao and JZ. Leibo and Youssef Mroueh and Tomaso Poggio} } @conference {222, title = {Learning invariant representations and applications to face verification}, booktitle = {NIPS 2013}, year = {2014}, month = {02/2014}, publisher = {Advances in Neural Information Processing Systems 26}, organization = {Advances in Neural Information Processing Systems 26}, address = {Lake Tahoe, Nevada}, abstract = {

One approach to computer object recognition and modeling the brain{\textquoteright}s ventral stream involves unsupervised learning of representations that are invariant to common transformations. However, applications of these ideas have usually been limited to 2D affine transformations, e.g., translation and scaling, since they are easiest to solve via convolution. In accord with a recent theory of transformation-invariance [1], we propose a model that, while capturing other common convolutional networks as special cases, can also be used with arbitrary identity-preserving transformations. The model{\textquoteright}s wiring can be learned from videos of transforming objects{\textemdash}or any other grouping of images into sets by their depicted object. Through a series of successively more complex empirical tests, we study the invariance/discriminability properties of this model with respect to different transformations. First, we empirically confirm theoretical predictions (from [1]) for the case of 2D affine transformations. Next, we apply the model to non-affine transformations; as expected, it performs well on face verification tasks requiring invariance to the relatively smooth transformations of 3D rotation-in-depth and changes in illumination direction. Surprisingly, it can also tolerate clutter {\textquotedblleft}transformations{\textquotedblright} which map an image of a face on one background to an image of the same face on a different background. Motivated by these empirical findings, we tested the same model on face verification benchmark tasks from the computer vision literature: Labeled Faces in the Wild, PubFig [2, 3, 4] and a new dataset we gathered{\textemdash}achieving strong performance in these highly unconstrained cases as well.

}, keywords = {Computer vision}, url = {http://nips.cc/Conferences/2013/Program/event.php?ID=4074}, author = {Qianli Liao and JZ. Leibo and Tomaso Poggio} } @article {384, title = {Subtasks of unconstrained face recognition}, year = {2014}, month = {01/2014}, abstract = {

This package contains:

1. \ SUFR-W, a dataset of {\textquotedblleft}in the wild{\textquotedblright} natural images of faces gathered from the internet. The protocol used to create the dataset is described in Leibo, Liao and Poggio (2014).

2. \ The full set of SUFR synthetic datasets, called the {\textquotedblleft}Subtasks of Unconstrained Face Recognition Challenge{\textquotedblright} in Leibo, Liao and Poggio (2014).

Click here for more information \& download \>

Click here to download the data set directly \>

}, keywords = {Computer vision}, author = {JZ. Leibo and Qianli Liao and Tomaso Poggio} } @article {226, title = {Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?}, number = {001}, year = {2014}, month = {03/2014}, abstract = {

The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n{\textrightarrow}$\infty$). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n{\textrightarrow}1), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based on the unsupervised, automatic learning of a {\textquotedblleft}good{\textquotedblright} representation for supervised learning, characterized by small sample complexity (n). We consider the case of visual object recognition though the theory applies to other domains. The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translations, scaling and other transformations can considerably reduce the sample complexity of learning. We prove that an invariant and unique (discriminative) signature can be computed for each image patch, I, in terms of empirical distributions of the dot-products between I and a set of templates stored during unsupervised learning. A module performing filtering and pooling, like the simple and complex cells described by Hubel and Wiesel, can compute such estimates. Hierarchical architectures consisting of this basic Hubel-Wiesel moduli inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts. The theory extends existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition{\textemdash}and that this representation may be continuously learned in an unsupervised way during development and visual experience.

}, keywords = {Computer vision, Pattern recognition}, author = {F. Anselmi and JZ. Leibo and Lorenzo Rosasco and Jim Mutch and Andrea Tacchetti and Tomaso Poggio} }