Young Ho Kwon

Bio: Young Ho Kwon is an academic researcher from University of Central Florida. The author has contributed to research in topics: Facial recognition system & Feature (computer vision). The author has an hindex of 6, co-authored 6 publications receiving 1570 citations.

Age classification from facial images

Abstract: The ability to classify age from a facial image has not been pursued in computer vision. This research addresses the limited task of age classification of a facial image into a baby, young adult, and senior adult. This is the first reported work to classify age, and to successfully extract and use natural wrinkles. We present a theory and practical computations for visual age classification from facial images, based on cranio-facial changes in feature-position ratios, and on skin wrinkle analysis. Three age groups are classified. >

Face detection using templates

Abstract: In the problem area of human facial image processing, the first computational task that needs to be solved is that of detecting a face under arbitrary scene conditions. Although some progress towards this has been reported in the literature, face detection remains a difficult problem. In this paper the authors report on a novel face-finding method that appears quite robust. First, "snakelets" are used to find candidate edges. Candidate ovals (face-locations) are then found from these snakelets using a voting method. For each of these candidate face-locations, the authors use a method introduced previously to find detailed facial features. If a substantial number of the facial features are found successfully, and their positions satisfy ratio-tests for being standard, the procedure positively reports the existence of a face at this location in the image.

Automatic feature detection and age classification of human faces in digital images

Abstract: The invention includes a four step process for automatically finding facial images of a human face in an electronically digitized image (for example, taken by a video-camera), and classifying the age of the person (associated with the face) into an age category. For example three age categories: a baby(up to approximately age 3), a junior person(above age 3 to approximately age forty), and a senior adult (over forty years old). Categories can be further subdivided whereas every three years could be a further age category. Step 1 of the process is to find facial features of the digital image encompassing the chin, sides of the face, virtual top of the head, eyes, mouth and nose of the image. Step 2 is to compute the facial feature ratios of the facial features ratios of the facial features found in Step 1. Step 3 is to compute a wrinkle analysis of the image. Step 4 is to combine the previous two steps to categorize age of the facial image. The invention can locate and detect facial images for age classification from digital camera images and computerized generated images. The invention can be practiced in areas such as population statistic gathering for patrons at entertainment/amusement parks, television viewer ratings. Furthermore, the invention has utility in automated security/surveillance systems, demographic studies, safety monitoring systems, computer human-interface operations and automated photography. The latter to allow for point and shoot focus on specific individuals as a function of their age classification.

Locating facial features for age classification

Abstract: In this paper, we outline computations for visual age classification from facial images. For now, input images can only be classified into one of three age-groups: babies, adults, and senior adults. The computations are based on cranio-facial development theory, and wrinkle analysis. In the implementation, first primary features of the face are found, followed by secondary feature analyses. Preliminary results with real data are presented.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Detecting faces in images: a survey

Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

Methods and Systems for Content Processing

Abstract: Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Recognizing action units for facial expression analysis

Abstract: Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an automatic face analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AU) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AU and 10 lower face AU) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AU and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AU. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.

Age and gender classification using convolutional neural networks

Abstract: Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.