International audienc

Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to autoencoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition.

Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We propose in this paper a new threshold adaptation system which uses a timestamp objective at which neurons should fire. We show that our method leads to state-of-the-art classification rates on the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an unsupervised SNN followed by a linear SVM. We also investigate the sparsity level of the network by testing different inhibition policies and STDP rules.

The continuous evolution of computer capacities, as well as the emergence of the X3D standard has recently boosted the 3D domain. Even if efficient tools that support the designer's work exist, little attention is paid to the reuse of 3D models. Associating some semantics with 3D contents is an important issue for reusing such contents or pieces of content. In this paper, we address this issue by using a generic semantic annotation model for 3D, called 3DSEAM [Bilasco et al. 2005b] (3D SEmantics Annotation Model). 3DSEAM aims at indexing 3D contents considering visual, geometric and semantic aspects. A generic 3D Annotation Framework (called 3DAF) is proposed in order to manage the semantic annotations of 3D objects. 3DAF is instantiated using an MPEG-7-based architecture. An extension of MPEG-7 that addresses 3D content is used. [Bilasco et al. 2005a].

In this paper, we propose a novel gender recognition framework based on a Fuzzy Inference System (FIS). Our main objective is to study the gain brought by FIS in presence of various visual sensors (e.g., hair, mustache, inner face). We use inner and outer facial features to extract input variables. First, we define the fuzzy statements and then we generate a knowledge base composed of a set of rules over the linguistic variables including hair volume, mustache and a vision-sensor. Hair volume and mustache information are obtained from Part Labels subset of Labeled Faces in the Wild (LFW) database and vision-sensor is obtained from a pixel-intensity based SVM+RBF classifier trained on di↵erent databases including Feret, Groups and GENKI-4K. Cross-database test experiments on LFW database showed that the proposed method provides better accuracy than optimized SVM+RBF only classification. We also showed that FIS increases the inter-class variability by decreasing False Negatives (FN) and False Positives (FP) using expert knowledge. Our experimental results yield an average accuracy of 93.35% using Groups/LFW test, while the SVM performance baseline yields 91.25% accuracy.

The evolving desktop computer capacities and the emergence of the X3D standard offer a new boost to 3D domain. Giving sense to 3D content becomes a major issue specially for reusing such a content extracted from existing 3D scenes. In this paper, we address this issue by proposing a generic semantic annotation model for 3D called 3DSEAM (3D SEmantics Annotation Model). 3DSEAM aims at indexing 3D content considering visual, geometric and semantic aspects. 3DSEAM is instantiated using MPEG-7 extended with 3D specific locators. These locators link the visual, geometric and semantic features of a 3D content to the corresponding X3D fragment.

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative Local Motion Patterns (LMP) feature. The LMP feature analyzes locally the motion distribution in order to separate consistent mouvement patterns from noise. Indeed, facial motion extracted from the face is generally noisy and without specific processing, it can hardly cope with expression recognition requirements especially for micro-expressions. Direction and magnitude statistical profiles are jointly analyzed in order to filter out noise. This work presents three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition leading the way to supporting a wide range of expression intensities. The third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variations and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-ranked state-of-the-art methods for macro expression recognition.

Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. Additionally, the strengths of multiple optical flow approaches are combined in a novel data augmentation scheme. Under this scheme, increases in average accuracy of up to 6% (depending on the choice of optical flow approaches and dataset) have been achieved.