Dynamic behavior analysis via structured rank minimization

Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach

Phase transitions in neural networks

The behaviour of computer simulations of networks of neuron-like binary decision elements is studied. The models are discrete in time and deterministic , but the sequence of states of neurons in a net is not generally reversible in time because of the threshold nature of neurons. Self-organisation, or activity-dependent modification of interneuronal connection strengths, is used. Cyclic modes of activity which emerge spontaneously, underlie possible mechanisms of short term memory and associative thinking. The transition from seemingly random activity patterns to cyclic activity is examined in isolated networks with pseudorandomly chosen connection matrices; and the transition is related to the gross properties of the network. Nets with inherent structure (from pseudorandom nature) and imposed structure are studied, when cycles of length greater than, say, 12 time units are considered separately from the less complex, shorter cycles; the aforementioned transitions appear to be consistently rapid, compared to the cycle length, unless architecture is imposed such that nearly independent groups of neurons exist in the same net

Neural network analysis and simulation

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First Steps Towards Automatic Recognition of Spontaneous Facial Action Units

We present ongoing work on a project for automatic recognition of spontaneous facial actions (FACs). Current methods for automatic facial expression recognition assume images are collected in controlled environments in which the subjects deliberately face the camera. Since people often nod or turn their heads, automatic recognition of spontaneous facial behavior requires methods for handling out-of-image-plane head rotations. There are many promising approaches to address the problem of out-of-image plane rotations. In this paper we explore an approach based on 3-D warping of images into canonical views. Since our goal is to explore the potential of this approach, we first tried with images with 8 handlabeled facial landmarks. However the approach can be generalized in a straight-forward manner to work automatically based on the output of automatic feature detectors. A front-end system was developed that jointly estimates camera parameters, head geometry and 3-D head pose across entire sequences of video images. Head geometry and image parameters were assumed constant across images and 3-D head pose is allowed to vary. First a a small set of images was used to estimate camera parameters and 3D face geometry. Markov chain Monte-Carlo methods were then used to recover the most-likely sequence of 3D poses given a sequence of video images. Once the 3D pose was known, we warped each image into frontal views with a canonical face geometry. We evaluate the performance of the approach as a front-end for an spontaneous expression recognition task