Accurate landmarking from 3D facial scans by CNN and cascade regression

2018 ◽  
Vol 16 (02) ◽  
pp. 1840007 ◽  
Author(s):  
Wang Kai ◽  
Jun An ◽  
Xi Zhao ◽  
Jianhua Zou
Keyword(s):  
Mapping Function ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
The Face ◽  
Geometry Feature ◽  
The Mean ◽  
Scan Data ◽  
Cascade Regression ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolutional neural network (CNN) to initialize the face landmarks instead of the mean shape. Then the proposed cascade regression networks learn the mapping function between 3D facial geometry feature and landmarks location. Tested on Bosphorus database, the experimental results demonstrated effectiveness and accuracy of the proposed method for [Formula: see text] landmarks. Compared with other methods, the results in several points demonstrate state-of-the-art performance.

scholarly journals A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 308 ◽  
Author(s):  
Kai Wang ◽  
Xi Zhao ◽  
Wanshun Gao ◽  
Jianhua Zou
Keyword(s):  
Feature Fusion ◽  
State Of The Art ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
Facial Landmarks ◽  
3D Data ◽  
Deep Feature ◽  
Local Patch ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.

Facial Feature Detection Algorithm Based on Main Characteristics of Eyes

2013 ◽  
Vol 303-306 ◽  
pp. 1402-1405 ◽  
Author(s):  
Chang Yuan Wang ◽  
Mei Juan Qu ◽  
Hong Bo Jia ◽  
Hong Zhe Bi
Keyword(s):  
Feature Detection ◽  
Search Algorithm ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Appearance Model ◽  
Feature Points ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
The Face ◽  
Face Contour

This paper proposed a new facial feature points localization algorithm based on main characteristics of eyes.Use the result of pupil center position to initialize the model of hybrid improved active shape model (ASM) and active appearance model (AAM). The algorithm will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it optimizes measure functions of ASM and AAM to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

Active Shape and Active Apparent Hybrid Model of Human Facial Feature Points Localization Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 2284-2287
Author(s):  
Chang Yuan Wang ◽  
Jing Wang ◽  
Mei Juan Qu
Keyword(s):  
Search Algorithm ◽  
Facial Feature ◽  
Appearance Model ◽  
Feature Points ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Contour Feature ◽  
Active Shape ◽  
The Face ◽  
Face Contour

An improved active shape model (ASM) and active appearance model (AAM) based new method is proposed, this method will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it uses different measure functions to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

The Difference in Facial Movement Between the Medial and the Lateral Midface: A 3D Skin Surface Vector Analysis

2021 ◽  
Author(s):  
David L Freytag ◽  
Michael G Alfertshofer ◽  
Konstantin Frank ◽  
Dmitry V Melnikov ◽  
Nicholas Moellhoff ◽  
...  
Keyword(s):  
3D Imaging ◽  
Functional Anatomy ◽  
Skin Surface ◽  
Facial Movements ◽  
Anatomic Location ◽  
Objective Evidence ◽  
The Face ◽  
Functional Boundary ◽  
The Mean ◽  
The Difference

Abstract Background Our understanding of the functional anatomy of the face is constantly improving. To date, it is unclear whether the anatomic location of the line of ligaments has any functional importance during normal facial movements such as smiling. Objectives It is the objective of the present study to identify differences in facial movements between the medial and lateral midface by means of skin vector displacement analyses derived from 3D imaging and to further ascertain whether the line of ligaments has both a structural and functional significance in these movements. Methods The study sample consisted of 21 healthy volunteers (9 females & 12 males) of Caucasian ethnic background with a mean age of 30.6 (8.3) years and a mean BMI of 22.57 (2.5) kg/m 2. 3D images of the volunteers’ faces in repose and during smiling (Duchenne type) were taken. 3D imaging-based skin vector displacement analyses were conducted. Results The mean horizontal skin displacement was 0.08 (2.0) mm in the medial midface (lateral movement) and was -0.08 (1.96) mm in the lateral midface (medial movement) (p = 0.711). The mean vertical skin displacement (cranial movement of skin toward the forehead/temple) was 6.68 (2.4) mm in the medial midface whereas it was 5.20 (2.07) mm in the lateral midface (p = 0.003). Conclusions The results of this study provide objective evidence for an antagonistic skin movement between the medial and the lateral midface. The functional boundary identified by 3D imaging corresponds to the anatomic location of the line of ligaments.

scholarly journals Facial Feature Model for a Portrait Video Stylization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 442 ◽  
Author(s):  
Dongxue Liang ◽  
Kyoungju Park ◽  
Przemyslaw Krompiec
Keyword(s):  
Facial Feature ◽  
Feature Model ◽  
Facial Features ◽  
Outer Face ◽  
Flow Estimation ◽  
Photorealistic Rendering ◽  
Stroke Models ◽  
The Face ◽  
Face Contour ◽  
Human Faces

With the advent of the deep learning method, portrait video stylization has become more popular. In this paper, we present a robust method for automatically stylizing portrait videos that contain small human faces. By extending the Mask Regions with Convolutional Neural Network features (R-CNN) with a CNN branch which detects the contour landmarks of the face, we divided the input frame into three regions: the region of facial features, the region of the inner face surrounded by 36 face contour landmarks, and the region of the outer face. Besides keeping the facial features region as it is, we used two different stroke models to render the other two regions. During the non-photorealistic rendering (NPR) of the animation video, we combined the deformable strokes and optical flow estimation between adjacent frames to follow the underlying motion coherently. The experimental results demonstrated that our method could not only effectively reserve the small and distinct facial features, but also follow the underlying motion coherently.

Studies on the storage of potatoes. III. The composition of the atmosphere in a potato clamp

1950 ◽  
Vol 40 (3) ◽  
pp. 227-232 ◽  
Author(s):  
E. M. Crook ◽  
D. J. Watson
Keyword(s):  
Multiple Regression ◽  
Wind Velocity ◽  
Practical Importance ◽  
Oxygen Depletion ◽  
Unsaturated Compounds ◽  
The Face ◽  
Flow Through ◽  
The Mean ◽  
Oxygen Concentrations

The CO2 concentration in the atmosphere of a potato clamp varied between 0·06 and 0·86%. The sum of CO2 and oxygen concentrations remained approximately constant at 21%. The CO2 concentration increased with time from December to April. This was attributed to increase in the rate of respiration of the potatoes caused by rise of temperature. Wind blowing in the direction normal to the face of the clamp reduced the COa concentration, presumably by causing external air to flow through the clamp coverings. A multiple regression of CO2 concentration on temperature of the potatoes at the time of sampling, and on the mean component of wind velocity normal to the clamp face estimated over a period of 3 hr. before the time of sampling, accounted for 64% of the variance between sampling occasions.Unsaturated compounds were detected in the clamp atmosphere by absorption in bromine; the concentration of these, expressed as ethylene, varied between 0·004 and 0·025%.The magnitude of CO2 accumulation and oxygen depletion in the clamp atmosphere was too small to produce effects of practical importance on the storage behaviour of the potatoes. If the unsaturated compounds were ethylene, the concentration present was sufficient to cause appreciable retardation of sprouting.

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