A 3-stage algorithm is proposed for automatic detection of the four primary landmarks in 3D face imagery: eyes, nose, and mouth. Pose and facial expression variations which raise major difficulties in landmark processing are the primary focus of this work. In the first stage, Gaussian and Mean curvatures are used to extract ridge and valley points. The second stage utilizes a recursive grouping algorithm to generate candidate landmarks. In the last stage, a geometric model imposing a set of distance and angle constraints to the arrangement of candidate landmarks is utilized to select the final four landmarks. The algorithm is robust against variations in pose and expression with an overall success rate of 98.3%, using the Bosphorus Database as the test input.

Biometric encryption (BE) has recently been identified as a promising paradigm to deliver security and privacy, with unique technical merits and encouraging social implications. An integral component in BE is a key binding method, which is the process of securely combining a signal, containing sensitive information to be protected (i.e., the key), with another signal derived from physiological features (i.e., the biometric). A challenge to this approach is the high degree of noise and variability present in physiological signals. As such, fuzzy methods are needed to enable proper operations, with adequate performance results in terms of false acceptance rate and false rejection rate. In this work, the focus will be on a class of fuzzy key binding methods based on dirty paper coding known as quantization index modulation. While the methods presented are applicable to a wide range of biometric modalities, the face biometric is selected for illustrative purposes, in evaluating the QIM-based solutions for BE systems. Performance evaluation of the investigated methods is reported using data from the CMU PIE face database.

This paper presents a scheme for secure coding of arbitrarily-shaped visual objects. Called SecST-SPIHT, it employs SPIHT based coding along with selective encryption for efficient, secure storage and transmission of visual object shape and texture. The selective encryption utilizes a novel bit classification scheme which ensures protection of the entire code by encrypting only a small number of bits (less than 5% for securing shape and texture, and less than 0.5% for just the texture). The encryption is performed in the compressed domain and does not affect the rate-distortion performance of the coder. A parameter allows control over the strength of the encryption versus required processing overhead. The scheme can be employed in a privacy protected surveillance system, whereby visual objects of human subjects are encrypted so that the content is only available to certain entities, such as persons of authority, possessing the correct decryption key.

A new scheme for coding both the shape and texture of arbitrarily shaped visual objects is presented. Based on set partitioning on hierarchical trees (SPIHT), the proposed Shape and Texture SPIHT (ST-SPIHT) employs a novel implementation of the shape-adaptive discrete wavelet transform (SA-DWT) using in-place lifting, along with parallel coding of texture coefficients and shape mask pixels to create a single embedded code that allows for fine-grained rate-distortion scalability. The single output code simplifies the logistics of object storage and transmission compared to previously published schemes. An input parameter provides control over the relative progression between shape and texture coding in the embedded code, allowing for adjustment of the emphasis of shape versus texture quality in low bit rate reconstructions. The combination of features provided by ST-SPIHT, namely, explicit and progressive shape coding in parallel with wavelet-based embedded coding of the object texture, is unique compared to previously published schemes. Computational complexity is minimized since the shape coding takes advantage of the decomposition and spatial orientation trees used for texture coding. Objective and subjective simulation results show that the proposed ST-SPIHT scheme has rate-distortion performance comparable or superior to MPEG-4 Visual Texture Coding for most bit rates

A postprocessing method for the correction of visual demosaicking artifacts is introduced. The restored, full-color images previously obtained by cost-effective color filter array interpolators are processed to improve their visual quality. Based on a localized color ratio model and the original underlying Bayer pattern structure, the proposed solution impressively removes false colors while maintaining image sharpness. At the same time, it yields excellent improvements in terms of objective image quality measures.