Face recognition based on facial landmark detection

Schild

Evolution and Human Behavior

2021

Research on social judgments of faces often investigates relationships between measures of face shape taken from images (facial metrics), and either perceptual ratings of the faces on various traits (e.g., attractiveness) or characteristics of the photographed individual (e.g., their health). A barrier to carrying out this research using large numbers of face images is the time it takes to manually position the landmarks from which these facial metrics are derived. Although research in face recognition has led to the development of algorithms that can automatically position landmarks on face images, the utility of such methods for deriving facial metrics commonly used in research on social judgments of faces has not yet been established. Thus, across two studies, we investigated the correlations between four facial metrics commonly used in social perception research (sexual dimorphism, distinctiveness, bilateral asymmetry, and facial width to height ratio) when measured from manually and automatically placed landmarks. In the first study, in two independent sets of open access face images, we found that facial metrics derived from manually and automatically placed landmarks were typically highly correlated, in both raw and Procrustes-fitted representations. In study two, we investigated the potential for automatic landmark placement to differ between White and East Asian faces. We found that two metrics, facial width to height ratio and sexual dimorphism, were better approximated by automatic landmarks in East Asian faces. However, this difference was small, and easily corrected with outlier detection. These data validate the use of automatically placed landmarks for calculating facial metrics to use in VALIDATING AUTOMATIC FACIAL LANDMARKS 3 research on social judgments of faces, but we urge caution in their use. We also provide a tutorial for the automatic placement of landmarks on face images.

Section: Study Two -Testing For Potential Biases In Automatic Landmarmentioning

confidence: 99%

mentioning

confidence: 99%

Facial metrics generated from manually and automatically placed image landmarks are highly correlated

Schild

Evolution and Human Behavior

2021

“…These approaches have the advantage of being able to detect landmarks quickly and accurately, and it is even possible to process the data in realtime in a general hardware environment [8,9]. These technologies can be used for various purposes such as face identification [10,11] and emotion recognition [12,13]. Although using five or 68 points is useful and practical, this approach does not detect hair, which plays a significant role in identifying an individual.…”

Section: Introductionmentioning

confidence: 99%

FLSNet: Robust Facial Landmark Semantic Segmentation

2020

The human face is one of the most viewed visual objects in a person's life and is used for identifying a person through facial landmarks, which includes the eyes, nose, mouth, and ears that make up a face. It is also possible to communicate nonverbally through the movements of facial landmarks; that is, change of facial expression. Thus, facial landmarks play a crucial role in human-related image analysis. Automatic facial landmark detection is a challenging problem in the field of computer vision, and various studies are underway. The emergence of Deep Neural Networks has played an important role in solving difficult problems in computer vision. Semantic segmentation is a field in which images are classified into pixel units and has also developed rapidly by incorporating deep learning. In this paper, we propose a method for accurately extracting facial landmarks using semantic segmentation. First, we introduce a semantic segmentation architecture for sophisticated landmark detection, and datasets composed of facial images and ground truth pairs. Then, we suggest how improve the performance of pixel classification by adjusting the imbalance of the number of pixels according to the face landmark. Through extensive experiments, we evaluated our approach using the metrics pixel accuracy and intersection over union.

“…The task of landmark localisation is well established within the domain of computer vision and widely applied within a variety of biometric systems. Biometric systems for person identification commonly apply facial [13,[31][32][33][34][35][36], ear [28] and hand [26] landmark localisation, where Fig.1 shows example of these landmark localisation variations. The landmark localisation task can be described as predicting n fiducial landmarks when given a target image, the human face is one common target for landmark localisation where semantically meaningful facial landmarks such as the eyes, nose, mouth and jaw line are predicted.…”

Section: Introductionmentioning

confidence: 99%

Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis

et al. 2020

While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.