In this paper, we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison with image size). We will show that this joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. A special case of our fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative face databases (in constrained and unconstrained environments) document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.

In this work we present a general framework for robust error estimation in face recognition. The proposed formulation allows the simultaneous use of various loss functions for modeling the residual in face images, which usually follows non-standard distributions, depending on the image capturing conditions. Our method extends the current vast literature offering flexibility in the selection of the residual modeling characteristics but, at the same time, considering many existing algorithms as special cases. As such, it proves robust for a range of error inducing factors, such as, varying illumination, occlusion, pixel corruption, disguise or their combinations. Extensive simulations document the superiority of selecting multiple models for representing the noise term in face recognition problems, allowing the algorithm to achieve near-optimal performance in most of the tested face databases. Finally, the multi-model residual representation offers useful insights into understanding how different noise types affect face recognition rates.

Video retrieval and video copy detection are well studied problems. The goal is to find the matching video in a database from a given query video. Typically, these query videos are short and aligning the query video is of secondary importance. Short sequences can be aligned using dynamic time warping. But, since time and memory usage increases quadratically with the length of the sequences, such process is not suitable for the alignment of two full length movies. A typical feature film is between 70 and 210 minutes long. Our goal is to find an accurate frame-by-frame alignment of a full length original film and a copy that has inserted and deleted sequences (e.g., commercial breaks or censorship), as well as differences in quality, format and framerate. We propose a fast, robust and memory efficient video sequence alignment algorithm which has linear space and time complexity.

Compressive Sensing (CS) suggests that, under certain conditions, a signal can be reconstructed using a small number of incoherent measurements. We propose a novel video CS framework based on Multiple Measurement Vectors (MMV) which is suitable for signals with temporal correlation such as video sequences. In addition, a CS circulant matrix is employed for fast reconstruction. Furthermore, the proposed framework allows the number of CS measurements associated with each frame to be chosen in the decoder rather than the encoder offering robustness compared to the multi-scale approaches. Experimental results on two video sequences exhibiting fast motion and occlusions, show the advantages of the proposed method over the current state-of-the-art in video CS.