Age Estimation of Faces in Videos Using Head Pose Estimation and Convolutional Neural Networks.

Forensic age estimation from aspartic acid racemization (AAR): Scrutiny of multiple source samples

This paper introduces our head pose estimation system that localizes nose-tip of the faces and estimate head poses in studio quality pictures. After the nose-tip in the training data are manually labeled, the appearance variation caused by head pose changes is characterized by tensor model. Given images with unknown head pose and nose-tip location, the nose-tip of the face is localized in a coarse-to-fine fashion, and the head pose is estimated simultaneously by the head pose tensor model. The image patches at the localized nose tips are then cropped and sent to two other head pose estimators based on LEA and PCA techniques. We evaluated our system on the Pointing'04 head pose image database. With the nose-tip location known, our head pose estimators can achieve 94 - 96% head pose classification accuracy(within ±15°). With nose-tip unknown, we achieves 85% nose-tip localization accuracy (within 3 pixels from the ground truth), and 81 - 84% head pose classification accuracy(within ±15°).

Most surveillance cameras have a wide-angle field of view and are situated unobtrusively at overhead positions. For this type of application, head pose estimation is very challenging because of the limitations of the quality and resolution of the incoming data. In addition, even though the absolute head pose is constant, the head pose in camera view changes depending upon the location of head with respect the camera. We present a solution to estimate absolute coarse head pose for wide-angle overhead cameras by integrating 3D head position and pose information. The work involves image-based learning, pose correction based on 3D position, and real-time multicamera integration of low-resolution imagery. The system can be applied to an active face catalogger to obtain the best view of the face for surveillance, to customer relationship management to record behavior in retail stores or to virtual reality as an input device.

This paper presents estimation of head pose angles from a single 2D face image using a 3D face model morphed from a reference face model. A reference model refers to a 3D face of a person of the same ethnicity and gender as the query subject. The proposed scheme minimizes the disparity between the two sets of prominent facial features on the query face image and the corresponding points on the 3D face model to estimate the head pose angles. The 3D face model used is morphed from a reference model to be more specific to the query face in terms of the depth error at the feature points. The morphing process produces a 3D face model more specific to the query image when multiple 2D face images of the query subject are available for training. The proposed morphing process is computationally efficient since the depth of a 3D face model is adjusted by a scalar depth parameter at feature points. Optimal depth parameters are found by minimizing the disparity between the 2D features of the query face image and the corresponding features on the morphed 3D model projected onto 2D space. The proposed head pose estimation technique was evaluated on two benchmarking databases: 1) the USF Human-ID database for depth estimation and 2) the Pointing'04 database for head pose estimation. Experiment results demonstrate that head pose estimation errors in nodding and shaking angles are as low as 7.93° and 4.65° on average for a single 2D input face image.

Head pose estimation has attracted many research interest in recent years. With the advent of deep learning, it is possible to predict the head pose accurately from the RGB images without the help of facial landmarks or depth information. However, existing head pose datasets often lack large pose head images, which extremely limits the development of head pose estimation algorithms. In this paper, we build the largescale head pose dataset (LHPD) including more than 140,000 images with the diverse and accurate head poses. The LHPD dataset includes the head images recorded from different shooting angles between the camera and the human body for the first time, which greatly expands the range of head pose compared to previous datasets. Therefore, the range of head pose can cover +/-90° for each Euler angle. The accurate and reliable head pose annotation is labeled by the motion capture system and careful calibration procedures. We then propose a head pose estimation method through fine-tuning the ResNet on the LHPD dataset when using the Euclidean distance of quaternions as the loss function. The results show that our method achieves better performance than current state-of-theart algorithms.

We developed a fast and robust head pose and gaze estimation system. This system can detect facial feature points and estimate 3D pose angles and gaze direction in various conditions including changes in facial expression, partial occlusion, etc. The system needs only one face image as input and doesn't need any special devices such as blinking LED or stereo camera. Moreover, no calibration process is needed. It shows 95% of head pose estimation accuracy and 81% of gaze estimation accuracy (when the error margin is 15 degrees). The processing time is approximately 15ms/frame (Pentium4 3.2 GHz). Acceptable range of facial pose is within +/- 60 degrees in yaw (left-right) and within +/- 30 degrees in pitch (up-down).

This paper presents a face quality assessment approach that selects the highest‐quality face image using a two‐stage process from video streaming. In high‐traffic environments, traditional face recognition methods can cause crowd congestion, emphasizing the need for unconscious face recognition, which requires no active cooperation from individuals. Due to the nature of unconscious face recognition, it is necessary to capture high‐quality face images. In this paper, the FSA‐Net head pose estimation network is enhanced to FSA‐Shared_Nadam by replacing the Adam optimizer with Nadam and improving stage fusion. In the first stage, FSA‐Shared_Nadam estimates head pose angles, MediaPipe detects facial landmarks to calculate eye distance and aspect ratios, and sharpness is calculated using the Laplacian operator. Images are considered valid if they meet the criteria. A model trains a face quality scoring formula, learning how different head pose angles affect face recognition accuracy. In the second stage, face images are clustered, and the formula is applied to select the highest‐scoring face within each cluster. The approach was tested across multiple datasets, and a simulated security checkpoint scenario was created for practical testing. The results demonstrate the effectiveness of the FSA‐Shared_Nadam head pose estimation algorithm and the proposed face quality assessment approach.

Perception of head pose is useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. In this paper, we propose a novel random forest based method for estimating head pose angles from single face images. In order to improve the effectiveness of the constructed head pose predictor, we introduce feature weighting and tree screening into the random forest training process. In this way, the features with more discriminative power are more likely to be chosen for constructing trees, and each of the trees in the obtained random forest usually has high pose estimation accuracy, while the diversity or generalization ability of the forest is not deteriorated. The proposed method has been evaluated on four public databases as well as a surveillance dataset collected by ourselves. The results show that the proposed method can achieve state-of-the-art pose estimation accuracy. Moreover, we investigate the impact of pose angle sampling intervals and heterogeneous face images on the effectiveness of trained head pose predictors.

DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children

International audience

Recently, soft biometric trait classification has been receiving more attention in the computer vision community due to its wide range of possible application areas. Most approaches in the literature have focused on trait classification in controlled environments, due to the challenges presented by real-world environments, i.e. arbitrary facial expressions, arbitrary partial occlusions, arbitrary and nonuniform illumination conditions and arbitrary background clutter. In recent years, trait classification has started to be applied to real-world environments, with some success. However, the focus has been on estimation from single images or video frames, without leveraging the temporal information available in the entire video sequence. In addition, a fixed set of features are usually used for trait classification without any consideration of possible changes in the facial features due to head pose changes. In this paper, we propose a temporal, probabilistic framework first to robustly estimate continuous head pose angles from real-world videos, and then use this pose estimate to decide on the appropriate set of frames and features to use in a temporal fusion scheme for soft biometric trait classification. Experiments performed on large, real-world video sequences show that our head pose estimator outperforms the current state-of-the-art head pose approaches (by up to 51%), whereas our head pose conditioned biometric trait classifier (for the case of gender classification) outperforms the current state-of-the-art approaches (by up to 31%).

Head pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel head pose estimation system by performing head region detection using the Kinect [2], followed by face detection, feature tracking, and finally head pose estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We propose to use the scene flow approach to estimate the head pose from 2D video sequences. This estimation is based upon a generic 3D head model through the prior knowledge of the head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our head pose estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.

Accurate 3D head pose estimation from a 2D image frame is an essential component of modern consumer technology (CT). It enables a better determination of user attentiveness and engagement and can support immersive audio and AR experiences. While deep learning methods have improved the accuracy of head pose estimation models, these depend on the accurate annotation of training data. The acquisition of real-world head pose data with a large variation of yaw, pitch and roll is a very challenging task. Available head-pose datasets often have limitations in terms of the number of data samples, image resolution, annotation accuracy and sample diversity (gender, race, age). In this work, a rendering pipeline is proposed to generate pixel-perfect synthetic 2D headshot images from high-quality 3D facial models with accurate pose angle annotations. A diverse range of variations in age, race, and gender are provided. The resulting dataset includes more than 300k pairs of RGB images with the corresponding head pose annotations. For every hundred 3D models there are multiple variations in pose, illumination and background. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation dataset BIWI. The results show training with purely synthetic data produced by the proposed methodology can achieve close to state-of-the-art results on the head pose estimation task and is better generalized for age, gender and racial diversity than solutions trained on `real-World' datasets.

In virtual environments, head pose and/or eye-gaze estimation can be employed to improve the visual experience of the user by enabling adaptive level of detail during rendering. In this study, we present a real-time system for rendering complex scenes in an immersive virtual environment based on head pose estimation and perceptual level of detail. In our system, the position and orientation of the head are estimated using stereo vision approach and markers placed on a pair of glasses used to view images projected on a stereo display device. The main innovation of our work is the incorporation of uncertainty estimates to improve the visual experience perceived by the user. The estimated pose and its uncertainty are used to determine the desired level of detail for different parts of the scene based on criteria originating from physiological and psychological aspects of human vision. Subject tests have been performed to evaluate our approach.

We present a method for head pose estimation for moving targets in multi-camera environments. Our approach utilizes an ensemble of exemplar classifiers for joint head detection and pose estimation and provides finer-grained predictions than previous approaches. We incorporate dynamic camera selection, which allows a variable number of cameras to be selected at each time step and provides a tunable trade-off between accuracy and speed. On a benchmark dataset for multi-camera head pose estimation, our method predicts head pan angle with a mean absolute error of ~ 8° for different moving targets.