Human face detection is often the first step in applications such as video surveillance, human computer interface, face recognition, and image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections.

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.

Abstract Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed. 1 1.

Abstract—Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others.

Abstract—This paper deals with the low-level joint processing of color and motion for robust face analysis within a feature-based approach. To gain robustness and contrast under unsupervised viewing conditions, a nonlinear color transform relevant for hue segmentation is derived from a logarithmic model. A hierarchical segmentation scheme is based on Markov random field modeling, that combines hue and motion detection within a spatiotemporal neighborhood. Relevant face regions are segmented without parameter tuning. The accuracy of the label fields enables not only face detection and tracking but also geometrical measurements on facial feature edges, such as lips or eyes. Results are shown both on typical test sequences and on various sequences acquired from micro- or mobile cameras. The efficiency of the method makes it suitable for real-time applications aiming at audiovisual communication in unsupervised environments. Index Terms—Face analysis, hue, liptracking, logarithmic color space, Markov random field, motion detection, segmentation. I.

Abstract—Machine recognition of human emotional state is an important component for efficient human-computer interaction. The majority of existing works address this problem by utilizing audio signals alone, or visual information only. In this paper, we explore a systematic approach for recognition of human emotional state from audiovisual signals. The audio characteristics of emo-tional speech are represented by the extracted prosodic, Mel-fre-quency Cepstral Coefficient (MFCC), and formant frequency fea-tures. A face detection scheme based on HSV color model is used to detect the face from the background. The visual information is represented by Gabor wavelet features. We perform feature selec-tion by using a stepwise method based on Mahalanobis distance. The selected audiovisual features are used to classify the data into their corresponding emotions. Based on a comparative study of different classification algorithms and specific characteristics of individual emotion, a novel multiclassifier scheme is proposed to boost the recognition performance. The feasibility of the proposed system is tested over a database that incorporates human subjects from different languages and cultural backgrounds. Experimental results demonstrate the effectiveness of the proposed system. The multiclassifier scheme achieves the best overall recognition rate of 82.14%. Index Terms—Audiovisual information, emotion recognition, multiclassifier. I.

To develop more satisfying image navigation systems, we need tools to construct a semantic bridge between the user and the database. In this paper we present an image indexing scheme and a query language, which allow the user to introduce a cognitive dimension to the search. At an abstract level, this approach consists of: 1) learning the "natural language" that humans speak to communicate their semantic experience of images, 2) understand the relationships between this language and objective measurable image attributes, and then 3) develop the corresponding feature extraction schemes. In our previous work we have conducted a number of subjective experiments in which we asked human subjects to group images, and then explain verbally why they did so [1]. The results of this study indicated that part of the abstraction involved in image interpretation is often driven by semantic categories, which can be broken into more tangible semantic entities, i.e. objective semantic indicators. By analyzing our experimental data, we identified some candidate semantic categories (i.e. portraits, people, crowds, cityscapes, landscapes, etc.), discovered their underlying semantic indicators (i.e. skin, sky, water, object, etc.), and derived important lowlevel image descriptors accounting for our perception of these indicators. In our recent work we have used these findings to develop a set of image features that match the way humans communicate image meaning, and a "semantic-friendly" query language for browsing and searching diverse collections of images. We have implemented our approach into an Internet search engine, ISee, and tested it on a large number of images. The results we obtained are very promising.

Colour is an important and useful feature for object tracking and recognition in computer vision. However, it has the difficulty that the colour of the object changes if the illuminant colour changes. But under known illuminant colour it becomes a robust feature. There are more and more computer vision applications tracking humans, for example in interfaces for human computer interaction or automatic camera men, where skin colour is an often used feature. Hence, it would be of significant importance to know the illuminant colour in such applications. This paper proposes a novel method to estimate the current illuminant colour from skin colour observations. The method is based on a physical model of reflections, the assumption that illuminant colours are located close to the Planckian locus, and the knowledge about the camera parameters. The method is empirically tested using real images. The average estimation error of the correlated colour temperature is as small as 180K. Applications are for example in colour based tracking to adapt to changes in lighting and in visualisation to re-render image colours to their appearance under canonical viewing conditions.

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper will focus on the significant improvements made to our face detection algorithm presented in [1]. Specifically, a novel approach to retrieve skin-like homogeneous regions will be presented, which will be later used to retrieve face images. Good results have been obtained for a large variety of video sequences.