Hong-Yuan Mark Liao

Multipurpose watermarking for image authentication and protection

We propose a novel multipurpose watermarking scheme, in which robust and fragile watermarks are simultaneously embedded, for copyright protection and content authentication. By quantizing a host images wavelet coefficients as masking threshold units (MTUs), two complementary watermarks are embedded using cocktail watermarking and they can be blindly extracted without access to the host image. For the purpose of image protection, the new scheme guarantees that, no matter what kind of attack is encountered, at least one watermark can survive well. On the other hand, for the purpose of image authentication, our approach can locate the part of the image that has been tampered with and tolerate some incidental processes that have been executed. Experimental results show that the performance of our multipurpose watermarking scheme is indeed superb in terms of robustness and fragility.

Facial feature detection using geometrical face model: An efficient approach

Abstract A useful geometrical face model and an efficient facial feature detection approach are proposed. Based on the fact that human faces are constructed in the same geometrical configuration, the proposed approach can accurately detect facial features, especially the eyes, even when the images have complex backgrounds. The average computation time for one image of size 512 × 340 is less than 5 s by a SUN-Sparc 20 workstation. Experimental results demonstrate that the proposed approach can efficiently detect human facial features and satisfactorily deal with the problems caused by bad lighting condition, skew face orientation, and even facial expression.

Image Registration Using a New Edge-Based Approach

A new edge-based approach for efficient image registration is proposed. The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. By using a line-fitting model, all the edge directions of the feature points are estimated from the edge outputs of a transformed image. In order to estimate the orientation difference between two partially overlapping images, a so-called “angle histogram” is calculated. From the angle histogram, the rotation angle which can be used to compensate for the difference between two target images can be decided by seeking the angle that corresponds to the maximum peak in the histogram. Based on the rotation angle, an initial matching can be performed. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided. The proposed method can tolerate roughly about 10% scaling variation and does not restrict the position and orientation of images. Further, since all the selected feature points are edge points, the restriction can significantly reduce the search space and, meanwhile, speed up the matching process. Compared with conventional algorithms, the proposed scheme is a great improvement in efficiency as well as reliability for the image registration problem.

Fast face detection via morphology-based pre-processing

Abstract An efficient face detection algorithm which can detect multiple faces oriented in any directions in a cluttered environment is proposed. In this paper, a morphology-based technique is first devised to perform eye-analogue segmentation. Next, the previously located eye-analogue segments are used as guides to search for potential face regions. Then, each of these potential face images is normalized to a standard size and fed into a trained backpropagation neural network for identification. In this detection system, the morphology-based eye-analogue segmentation process is able to reduce the background part of a cluttered image by up to 95%. This process significantly speeds up the subsequent face detection procedure because only 5–10% of the regions of the original image remain for further processing. Experiments demonstrate that an approximately 94% success rate is reached, and that the relative false detection rate is very low.

Cocktail Watermarking on Images

A novel image protection scheme named “cocktail watermarking” improves over current spread-spectrum watermarking approaches. Two watermarks, which play complementary roles, are simultaneously embedded into an original image. The new watermarking scheme has the characteristic that, no matter what an attack is, at least one watermark typically survives well and can be detected. Results of extensive experiments indicate that our cocktail watermarking scheme is effective in resisting various attacks.

Video-Based Human Movement Analysis and Its Application to Surveillance Systems

This paper presents a novel posture classification system that analyzes human movements directly from video sequences. In the system, each sequence of movements is converted into a posture sequence. To better characterize a posture in a sequence, we triangulate it into triangular meshes, from which we extract two features: the skeleton feature and the centroid context feature. The first feature is used as a coarse representation of the subject, while the second is used to derive a finer description. We adopt a depth-first search (dfs) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is more robust and efficient than conventional silhouette-based approaches. The skeletal features extracted in the first stage are used to extract the centroid context feature, which is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process because they generate a set of key postures from a movement sequence. The ordered key posture sequence is represented by a symbol string. Matching two arbitrary action sequences then becomes a symbol string matching problem. Our experiment results demonstrate that the proposed method is a robust, accurate, and powerful tool for human movement analysis.

Wavelet-Based Off-Line Handwritten Signature Verification

In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.

Personalized travel recommendation by mining people attributes from community-contributed photos

Leveraging community-contributed data (e.g., blogs, GPS logs, and geo-tagged photos) for travel recommendation is one of the active researches since there are rich contexts and trip activities in such explosively growing data. In this work, we focus on personalized travel recommendation by leveraging the freely available community-contributed photos. We propose to conduct personalized travel recommendation by further considering specific user profiles or attributes (e.g., gender, age, race). In stead of mining photo logs only, we argue to leverage the automatically detected people attributes in the photo contents. By information-theoretic measures, we will demonstrate that such people attributes are informative and effective for travel recommendation -- especially providing a promising aspect for personalization. We effectively mine the demographics for different locations (or landmarks) and travel paths. A probabilistic Bayesian learning framework which further entails mobile recommendation on the spot is introduced. We experiment on four million photos collected for eight major worldwide cities. The experiments confirm that people attributes are promising and orthogonal to prior works using travel logs only and can further improve prior travel recommendation methods especially in difficult predictions by further leveraging user contexts in mobile devices.

Video stabilization for a camcorder mounted on a moving vehicle

Vision systems play an important role in many intelligent transportation systems (ITS) applications, such as traffic monitoring, traffic law reinforcement, driver assistance, and automatic vehicle guidance. These systems installed in either outdoor environments or vehicles have often suffered from image instability. In this paper, a video stabilization technique for a camcorder mounted on a moving vehicle is presented. The proposed approach takes full advantage of the a priori information of traffic images, significantly reducing the computational and time complexities. There are four major steps involved in the proposed approach: global feature extraction, camcorder motion estimation, motion taxonomy, and image compensation. We begin with extracting the global features of lane lines and the road vanishing point from the input image. The extracted features are then combined with those detected in previous images to compute the camcorder motion corresponding to the current input image. The computed motion consists of both expected and unexpected components. They are discriminated and the expected motion component is further smoothed. The resulting motion is next integrated with a predicted motion, which is extrapolated from the previous desired camcorder motions, leading to the desired camcorder motion associated with the input image under consideration. The current input image is finally stabilized based on the computed desired camcorder motion using an image transformation technique. A series of experiments with both real and synthetic data have been conducted. The experimental results have revealed the effectiveness of the proposed technique.

Mean-quantization-based fragile watermarking for image authentication

The authors propose an image authentication scheme, which is able to detect malicious tampering while tolerating some incidental distortions. By modeling the magnitude changes caused by incidental distortion and malicious tampering as Gaussian distributions with small and large variances, respectively, they propose to embed a watermark by using a mean-quantization technique in the wavelet domain. The pro- posed scheme is superior to the conventional quantization-based ap- proaches in credibility of authentication. Statistical analysis is conducted to show that the probabilities of watermark errors caused by malicious tampering and incidental distortion will be, respectively, maximized and minimized when the new scheme is applied. Experimental results dem- onstrate that the credibility of the method is superior to that of the con- ventional quantization-based methods under malicious attack followed by an incidental modification, such as JPEG compression, sharpening or blurring.