A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors

2010 ◽  
Author(s):  
David Monzo ◽  
Alberto Albiol ◽  
Antonio Albiol ◽  
Jose M. Mossi
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Landmark Localization

scholarly journals FAN-Face: a Simple Orthogonal Improvement to Deep Face Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12621-12628 ◽  
Author(s):  
Jing Yang ◽  
Adrian Bulat ◽  
Georgios Tzimiropoulos
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Simple Approach ◽  
Feature Integration ◽  
Prior Work ◽  
Shape Information ◽  
Facial Landmarks ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Landmark Localization

It is known that facial landmarks provide pose, expression and shape information. In addition, when matching, for example, a profile and/or expressive face to a frontal one, knowledge of these landmarks is useful for establishing correspondence which can help improve recognition. However, in prior work on face recognition, facial landmarks are only used for face cropping in order to remove scale, rotation and translation variations. This paper proposes a simple approach to face recognition which gradually integrates features from different layers of a facial landmark localization network into different layers of the recognition network. To this end, we propose an appropriate feature integration layer which makes the features compatible before integration. We show that such a simple approach systematically improves recognition on the most difficult face recognition datasets, setting a new state-of-the-art on IJB-B, IJB-C and MegaFace datasets.

scholarly journals Facial Expression Detection and Recognition through VIOLA-JONES Algorithm and HCNN using LSTM Method

2021 ◽  
pp. 463-480
Author(s):  
Dinesh Kumar P ◽  
Dr. B. Rosiline Jeetha
Keyword(s):  
Facial Expression ◽  
Expression Recognition ◽  
Scale Invariant ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Invariant Feature ◽  
Scale Invariant Feature ◽  
Localization Approach ◽  
Landmark Localization ◽  
Detection And Recognition

Facial expression, as one of the most significant means for human beings to show their emotions and intensions in the process of communication, plays a significant role in human interfaces. In recent years, facial expression recognition has been under especially intensive investigation, due conceivably to its vital applications in various fields including virtual reality, intelligent tutoring system, health-care and data driven animation. The main target of facial expression recognition is to identify the human emotional state (e.g., anger, contempt, disgust, fear, happiness, sadness, and surprise ) based on the given facial images. This paper deals with the Facial expression detection and recognition through Viola-jones algorithm and HCNN using LSTM method. It improves the hypothesis execution enough and meanwhile inconceivably reduces the computational costs. In feature matching, the author proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. For face detection, basically we utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the Hybrid Convolutional Neural Network (HCNN) and Long Short-Term Memory (LSTM) model for identifying the facial expression in efficient manner.The experimental result confirms that the HCNN-LSTM Model beats traditional deep-learning and machine-learning techniques with respect to precision, recall, f-measure, and accuracy using CK+ database. Proposes Hybrid Scale-Invariant Feature Transform (SIFT) with double δ-LBP (Dδ-LBP) and it utilizes the fixed facial landmark localization approach and SIFT’s orientation assignment, to obtain the features that are illumination and pose independent. And HCNN and LSTM model for identifying the facial expression.

Explicit Shape Regression With Characteristic Number for Facial Landmark Localization

2018 ◽  
Vol 20 (3) ◽  
pp. 567-579 ◽  
Author(s):  
Xin Fan ◽  
Risheng Liu ◽  
Zhongxuan Luo ◽  
Yuntao Li ◽  
Yuyao Feng
Keyword(s):  
Characteristic Number ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Shape Regression ◽  
Landmark Localization

scholarly journals An improved boundary-aware face alignment using stacked dense U-Nets

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094090
Author(s):  
Jianghao Ye ◽  
Ying Cui ◽  
Xiang Pan ◽  
Herong Zheng ◽  
Dongyan Guo ◽  
...  
Keyword(s):  
Message Passing ◽  
Face Alignment ◽  
Shape Variation ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Public Datasets ◽  
Pose Variation ◽  
Two Stages ◽  
Landmark Localization ◽  
Boundary Lines

Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.

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