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338
The science of emotional intelligence
, 2005
"... This article presents a framework for emotiolllJl intelligenCl!, a set of skills hypothesized to contribute to the accurate appraisal and expression of emotion in oneself and in others, the effective regulation of emotion in self and others, and the use of feelings to motivate, plan, and achieve in ..."
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Cited by 887 (38 self)
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This article presents a framework for emotiolllJl intelligenCl!, a set of skills hypothesized to contribute to the accurate appraisal and expression of emotion in oneself and in others, the effective regulation of emotion in self and others, and the use of feelings to motivate, plan, and achieve in one's life. We start by reviewing the debate about the adaptive versus maladaptive qualities of emotion. We then explore the literature on intelligence, and especiaUy social intelligence. to examine the place of emotion in traditional intelligence conceptions. A framework for integrating the research on emotion-related snUs Is then described. Next, we review the components of emotional intelligence. To conclude the review. the role of emotional intelligence in mental health is discussed and avenues for further investigation are suggested. Is "emotional intelligence " 8 contradiction in terms? One tradition in Western thought has viewed emotions as disorganized interruptions of mental activity, so potentially disruptive that they must be controlled. Writing in the first century B.C., Publilius Syrus stated, "Rule your feelings, lest your feelings rule you " [1}.
A survey on pixel-based skin color detection techniques
- In ICCGV
, 2003
"... Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Image content filtering, content-aware video compression and image color balancing applications can also benefit from automatic detection of skin in images. Numerous techniques for skin color modelling ..."
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Cited by 183 (2 self)
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Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Image content filtering, content-aware video compression and image color balancing applications can also benefit from automatic detection of skin in images. Numerous techniques for skin color modelling and recognition have been proposed during several past years. A few papers comparing different approaches have been published [Zarit et al. 1999], [Terrillon et al. 2000], [Brand and Mason 2000]. However, a comprehensive survey on the topic is still missing. We try to fill this vacuum by reviewing most widely used methods and techniques and collecting their numerical evaluation results.
Recent advances in visual and infrared face recognition—a review
- CVIU
, 2005
"... 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 infr ..."
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Cited by 105 (9 self)
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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.
A Bayesian Discriminating Features Method for Face Detection
, 2003
"... This paper presents a novel Bayesian Discriminating Features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes ..."
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Cited by 52 (6 self)
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This paper presents a novel Bayesian Discriminating Features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1-D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes "the rest of the world". The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution. Finally, the Bayes classifier applies the estimated conditional PDFs to detect multiple frontal faces in an image. Experimental results using 887 images (containing a total of 1,034 faces) from diverse image sources show the feasibility of the BDF method. In particular, the novel BDF method achieves 98.5% face detection accuracy with one false detection.
Biometric recognition using 3d ear shape
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear b ..."
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Cited by 40 (0 self)
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Abstract—Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes. Index Terms—Biometrics, ear biometrics, 3D shape, skin detection, curvature estimation, active contour, iterative closest point. 1
Smart home care network using sensor fusion and distributed vision-based reasoning
- In Proc. of VSSN 2006
, 2006
"... A wireless sensor network employing multiple sensing and event detection modalities and distributed processing is proposed for smart home monitoring applications. Image sensing and vision-based reasoning are employed to verify and further analyze events reported by other sensors. The system has been ..."
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Cited by 34 (3 self)
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A wireless sensor network employing multiple sensing and event detection modalities and distributed processing is proposed for smart home monitoring applications. Image sensing and vision-based reasoning are employed to verify and further analyze events reported by other sensors. The system has been developed to address the growing application domain in caregiving to the elderly and persons in need of monitored living, who care to live independently while enjoying the assurance of timely access to caregivers when needed. An example of sensed events is the accidental fall of the person under care. A wireless badge node acts as a bridge between the user and the network. The badge node provides user-centric event sensing functions such as detecting falls, and also provides a voice communication channel between the user and the caregiving center when the system detects an alert and dials the center. The voice connection is carried over an IEEE 802.15.4 radio link between the user badge and another node in the network that acts as a modem. Using signal strength measurements, the network nodes keep track of the approximate location of the user in the monitoring environment. The network also includes wall-mounted image sensor nodes, which are triggered upon detection of a fall to analyze their field-of-view and provide the caregiving center with further information about the user’s status. A description of the developed network and several examples of the vision-based reasoning algorithm are presented in the paper.
Detection and Tracking of Humans by Probabilistic Body Part Assembly
- Proc. of British Machine Vision Conference
, 2005
"... This paper presents a probabilistic framework of assembling detected human body parts into a full 2D human configuration. The face, torso, legs and hands are detected in cluttered scenes using boosted body part detectors trained by AdaBoost. Body configurations are assembled from the detected parts ..."
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Cited by 31 (0 self)
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This paper presents a probabilistic framework of assembling detected human body parts into a full 2D human configuration. The face, torso, legs and hands are detected in cluttered scenes using boosted body part detectors trained by AdaBoost. Body configurations are assembled from the detected parts using RANSAC, and a coarse heuristic is applied to eliminate obvious outliers. An a priori mixture model of upper-body configurations is used to provide a pose likelihood for each configuration. A joint-likelihood model is then determined by combining the pose, part detector and corresponding skin model likelihoods. The assembly with the highest likelihood is selected by RANSAC, and the elbow positions are inferred. This paper also illustrates the combination of skin colour likelihood and detection likelihood to further reduce false hand and face detections. 1
Color-Based Face Detection in the "15 Seconds of Fame" Art Installation
- in Proceedings of Mirage 2003, (INRIA Rocquencourt
, 2003
"... seconds of fame " is an interactive art installation which elevates the face of a randomly selected observer for 15 seconds into a "work of art". The installation was inspired by Andy Warhol's statement that "In the future everybody will be famous for 15 minutes" as wel ..."
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Cited by 31 (5 self)
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seconds of fame " is an interactive art installation which elevates the face of a randomly selected observer for 15 seconds into a "work of art". The installation was inspired by Andy Warhol's statement that "In the future everybody will be famous for 15 minutes" as well as by the pop-art style of his works. The critical part of the installation is detection of faces which enables the cropping of faces from the wide-angle image of people standing in front of the installation. In the paper we describe color-based face detection in detail and many problems that had to be solved to make it reliable in different illumination condi- tions and scenes.
Face localization and authentication using color and depth images
- IEEE Trans. Image Process
, 2005
"... Abstract—This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth ..."
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Cited by 31 (6 self)
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Abstract—This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of background clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information. Index Terms—Depth maps, embedded hidden Markov models, face authentication, face localization, face recognition, fusion, illumination, pose, synthetical views. I.
A fast multi-modal approach to facial feature detection
- IEEE WACV
, 2005
"... As interest in 3D face recognition increases the importance of the initial alignment problem does as well. In this paper we present a method utilizing the registered 2D color and range image of a face to automatically identify the eyes, nose, and mouth. These features are important to initially alig ..."
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Cited by 28 (2 self)
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As interest in 3D face recognition increases the importance of the initial alignment problem does as well. In this paper we present a method utilizing the registered 2D color and range image of a face to automatically identify the eyes, nose, and mouth. These features are important to initially align faces in both standard 2D and 3D face recognition algorithms. For our algorithm to run as fast as possible, we focus on the 2D color information. This allows the algorithm to run in approximately 4 seconds on a 640X480 image with registered range data. On a database of 1,500 images the algorithm achieved a facial feature detection rate of 99.6 % with 0.4 % of the images skipped
