Face Ellipse Research Articles

The Effects of Head Pose and Face Roundness on Age Progression in Children Faces

Background: This paper analyzes the geometric changes in human face during childhood to estimate the related age; cranial changes are used as age-progression features within childhood stage (0-12) years. Infant face is close to the circular shape turning to an ellipse shape over age progression face oval is determined and drawn using face landmarks, were it’s robust against opening mouth, thinness, fatness or face occlusion by hair.
 Materials and Methods: The experiments depending on two types of face dataset. First one is the standard FG-NET dataset, which was provided with face landmark points numbered from 1 to 68 [14, 15]. Besides, an Internet-based collected data set of (3010) face images extracted from Daily Photo Project.
 Results: Drawn face ellipse provided set of measures that significantly modeled changes in forehead size and face roundness. Studied age period was between birth and 12 years old. Exaggerated head size at birth provides round face with big forehead, which starts shrinking as age progresses natural face.
 Conclusion: Face ellipse provided efficient measures and distances to represent face changes along childhood. Comparing with published researches in young-face age estimation, proposed face ellipse recorded encouraging results.

Real-Time Gender Classification by Face

The identification of human beings based on their biometric body parts, such as face, fingerprint, gait, iris, and voice, plays an important role in electronic applications and has become a popular area of research in image processing. It is also one of the most successful applications of computer–human interaction and understanding. Out of all the abovementioned body parts,the face is one of most popular traits because of its unique features.In fact, individuals can process a face in a variety of ways to classify it by its identity, along with a number of other characteristics, such as gender, ethnicity, and age. Specifically, recognizing human gender is important because people respond differently according to gender. In this paper, we present a robust method that uses global geometry-based features to classify gender and identify age and human beings from video sequences. The features are extracted based on face detection using skin color segmentation and the computed geometric features of the face ellipse region. These geometric features are then used to form the face vector trajectories, which are inputted to a time delay neural network and are trained using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) function. Results show that using the suggested method with our own dataset under an unconstrained condition achieves a 100% classification rate in the training set for all application, as well as 91.2% for gender classification, 88% for age identification, and 83% for human identification in the testing set. In addition, the proposed method establishes the real-time system to be used in three applications with a simple computation for feature extraction.

3D Face pose estimation and tracking from a monocular camera

Abstract In this paper, we describe a new approach for estimating and tracking three-dimensional (3D) pose of a human face from the face images obtained from a single monocular view with full perspective projection. We assume that the shape of a 3D face can be approximated by an ellipse and that the aspect ratio of 3D face ellipse is given. Given a monocular image of a face, we first perform an ellipse detection to locate the face in the image and the 3D position and orientation of the face are then estimated from the detected image face. The face detection is greatly facilitated by exploring the physiological properties of eyes under a special IR illumination and some geometric constraints. The detected initial face ellipse is then tracked in subsequent frames, allowing to track 3D face pose from frame to frame. Compared with the existing feature-based approaches for face pose estimation, our approach is more robust, since ellipse can be more reliably and robustly detected and tracked. Experimental study using a large number of synthetic and real images demonstrates the accuracy and robustness of the proposed approach.