Handwritten text line segmentation on real-world data presents significant challenges that cannot be overcome by any single technique. Given the diversity of approaches and the recent advances in ensemble-based combination for pattern recognition problems, it is possible to improve the segmentation performance by combining the outputs from different line finding methods. In this paper, we propose a novel graph clustering-based approach to combine the output of an ensemble of text line segmentation algorithms. A weighted undirected graph is constructed with nodes corresponding to connected components and edge connecting pairs of connected components. Text line segmentation is then posed as the problem of minimum cost partitioning of the nodes in the graph such that each cluster corresponds to a unique line in the document image. Experimental results on a challenging Arabic field dataset using the ensemble method shows a relative gain of 18% in the F1 score over the best individual method within the ensemble.

Combining features from multiple, heterogeneous, audio visual sources can significantly improve retrieval performance in consumer domain videos. However, such videos often contain unrelated overlaid audio content, or have significant camera motion to reliably extract visual features. We present an approach, which overcomes errors in individual feature streams by combining classifiers trained on multiple, heterogeneous feature streams using Bayesian model combination (BAYCOM). We demonstrate our method, by combining low-level audio and visual features, for classification of a large 200 hour web video corpus. The combined models outperform any of the individual features by 10%. Further, BAYCOM consistently outperforms traditional early and late fusion methods.

In this paper, we propose a method to model the material constants (Young's modulus) of the skin in subregions of the face from the motion observed in multiple facial expressions and present its relevance to an image analysis task such as face verification. On a public database consisting of 40 subjects undergoing some set of facial motions associated with anger, disgust, fear, happy, sad, and surprise expressions, we present an expression invariant strategy to matching faces using the Young's modulus of the skin. Results show that it is indeed possible to match faces across expressions using the material properties of their skin.

This paper presents a novel method for automatic spotting (temporal segmentation) of facial expressions in long videos comprising of continuous and changing expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field of each subjects face. Testing has been done on 2 datasets (which includes 100 macro-expressions) and promising results have been obtained. The method is robust to several common drawbacks found in automatic facial expression segmentation including moderate in-plane and out-of-plane motion. Additionally, the method has also been modified to work with videos containing micro-expressions. Micro-expressions are detected utilizing their smaller spatial and temporal extent. A subject's face is divided in to sub-regions (mouth, cheeks, forehead, and eyes) and facial strain is calculated for each of these regions. Strain patterns in individual regions are used to identify subtle changes which facilitate the detection of micro-expressions.

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

We present a finite element modeling based approach to compute strain patterns caused by facial deformation during expressions in videos. A sparse motion field computed through a robust optical flow method drives the FE model. While the geometry of the model is generic, the material constants associated with an individualpsilas facial skin are learned at a coarse level sufficient for accurate strain map computation. Experimental results using the computational strategy presented in this paper emphasize the uniqueness and stability of strain maps across adverse data conditions (shadow lighting and face camouflage) making it a promising feature for image analysis tasks that can benefit from such auxiliary information.

The success of forensic identification largely depends on the availability of strong evidence or traces that substantiate the prosecution hypothesis that a certain person is guilty of crime. In light of this, extracting subtle evidences which the criminals leave behind at the crime scene will be of valuable help to investigators. We propose a novel method of using strain pattern extracted from changing facial expressions in video as an auxiliary evidence for person identification. The strength of strain evidence is analyzed based on the increase in likelihood ratio it provides in a suspect population. Results show that strain pattern can be used as a supplementary biometric evidence in adverse operational conditions such as shadow lighting and face camouflage where pure intensity-based face recognition algorithms will fail