Facial Landmarks Research Articles

Mapping the localization of the facial artery and vein at the inferior border of the mandible for clinical applications.

To spatially map the position of the facial artery and vein along the inferior border of the mandible based on specific facial reference points. This cross-sectional study included 40 sides of 21 formalin-fixed cadaver heads (11 female, 10 male) with a mean age of 72.92 ± 11.72years. Superficial dissection was performed to visualize the facial artery and vein at the inferior border of the mandible (FA and FV). Measurements were taken from the FA and FV to various facial landmarks using a digital caliper and medical protractor. Statistical analysis was conducted using Shapiro-Wilk, independent sample t-tests, and paired sample t-tests. The mean distance between FA and FV was 6.20 ± 2.97mm. Distances from FA and FV to gnathion, oral commissure, nasal wing, lateral canthus, angle of mandible, intertragic notch, and Manson's point were determined, with no significant differences found between sides and genders (p > 0.05). The closest distances from FA and FV to the line between the intertragic notch and oral commissure were 38.63 ± 4.86mm and 37.78 ± 5.28mm, respectively. The angular measurements of FA and FV with inferior border of mandible were 61.45 ± 13.71 and 76.56 ± 10.17 degrees, respectively. The angle between intertragic notch, FA and oral commisure was 102.40 ± 11.12 degrees. The detailed measurements and analysis provided in this study aim to enhance the precision of surgical interventions involving the facial artery and vein. The practical localization methods proposed can assist in minimizing the risk of vascular injuries, improving outcomes in reconstructive and aesthetic procedures.

Quantitative analysis of facial shape in children to support respirator design

The COVID-19 pandemic demonstrated the need for respiratory protection against airborne pathogens. Respirator options for children are limited, and existing designs do not consider differences in facial shape or size. We created a dataset of children's facial images from three cohorts, then used geometric morphometric analyses of dense and sparse facial landmark representations to quantify age, sex and ancestry-related variation in shape. We found facial shape and size in children vary significantly with age from ages 2 to 18, particularly in dimensions relevant to respirator design. Sex differences are small throughout most of the age range of our sample. Ancestry is associated with significant facial shape variation in dimensions that may affect respirator fit. We offer guidance on how to our results can be used for the appropriate design of devices such as respirators for pediatric populations. We also highlight the need to consider ancestry-related variation in facial morphology to promote equitable, inclusive products.

A Two-Stream Method for Human Action Recognition Using Facial Action Cues

Human action recognition (HAR) is a critical area in computer vision with wide-ranging applications, including video surveillance, healthcare monitoring, and abnormal behavior detection. Current HAR methods predominantly rely on full-body data, which can limit their effectiveness in real-world scenarios where occlusion is common. In such situations, the face often remains visible, providing valuable cues for action recognition. This paper introduces Face in Action (FIA), a novel two-stream method that leverages facial action cues for robust action recognition under conditions of significant occlusion. FIA consists of an RGB stream and a landmark stream. The RGB stream processes facial image sequences using a fine-spatio-multitemporal (FSM) 3D convolution module, which employs smaller spatial receptive fields to capture detailed local facial movements and larger temporal receptive fields to model broader temporal dynamics. The landmark stream processes facial landmark sequences using a normalized temporal attention (NTA) module within an NTA-GCN block, enhancing the detection of key facial frames and improving overall recognition accuracy. We validate the effectiveness of FIA using the NTU RGB+D and NTU RGB+D 120 datasets, focusing on action categories related to medical conditions. Our experiments demonstrate that FIA significantly outperforms existing methods in scenarios with extensive occlusion, highlighting its potential for practical applications in surveillance and healthcare settings.

Investigating Digital Illiterate Classification Techniques Based on DeepFace Technology

This paper presents an algorithm to identify digital illiterates by analyzing age and emotions from facial recognition. In this paper, digital illiterates refer to people who struggle in using digital devices. The study assumed that older individuals who display surprised or angry facial expressions while using digital devices are more likely to be identified as digitally illiterate. For age detection, the study used MTCNN (Multi-task Cascaded Convolutional Networks), and for emotion detection, it employed the VGG-Face model. MTCNN detects facial features and landmarks to preprocess images and distinguish facial characteristics. The VGG-Face model uses convolution operations to analyze facial images and classify emotional states. The dataset used in the study consisted of 3,000 facial images collected from the internet. The research team categorized the images into faces of individuals aged over 50, angry expressions, and surprised expressions. The dataset included 411 individuals (13.7%) aged over 50, 163 individuals (5.4%) with angry expressions, and 145 individuals (4.8%) with surprised expressions. Accuracy was estimated by comparing the results from the DeepFace algorithm with those from the research team’s classifications. The DeepFace algorithm achieved 95.77% accuracy in detecting individuals aged over 50, 83.45% accuracy for surprise, and 76.07% for anger. The results demonstrate that it is possible to identify digital illiterates based on their age and emotional expressions and could enable the development of personalized services to directly or indirectly support digital illiterates, potentially improving digital accessibility.

Facia-fix: mobile application for bell’s palsy diagnosis and assessment using computer vision and deep learning

Facial paralysis (FP) is a condition characterized by the inability to move some or all of the muscles on one or both sides of the face. Diagnosing FP presents challenges due to the limitations of traditional methods, which are time-consuming, uncomfortable for patients, and require specialized clinicians. Additionally, more advanced tools are often uncommonly available to all healthcare providers. Early and accurate detection of FP is crucial, as timely intervention can prevent long-term complications and improve patient outcomes. To address these challenges, our research introduces Facia-Fix, a mobile application for Bell's palsy diagnosis, integrating computer vision and deep learning techniques to provide real-time analysis of facial landmarks. The classification algorithms are trained on the publicly available YouTube FP (YFP) dataset, which is labeled using the House-Brackmann (HB) method, a standardized system for assessing the severity of FP. Different deep learning models were employed to classify the FP severity, such as MobileNet, CNN, MLP, VGG16, and Vision Transformer. The MobileNet model which uses transfer learning, achieved the highest performance (Accuracy: 0.9812, Precision: 0.9753, Recall: 0.9727, F1 Score: 0.974), establishing it as the optimal choice among the evaluated models. The innovation of this approach lies in its use of advanced deep learning models to provide accurate, objective, non-invasive and real-time comprehensive quantitative assessment of FP severity. Preliminary results highlight the potential of Facia-Fix to significantly improve the diagnostic and follow-up experiences for both clinicians and patients.

Soft Tissue Facial Morphology in Growing Patients with Different Occlusal Classes.

The study of facial profiles in the dental field is very important for the diagnosis and the dental and orthodontic treatment plan. The aim of this study is to analyze the three-dimensional morphology of the faces of 269 growing patients with Class I and II occlusions, focusing on children aged between 6 and 9 years old. The analysis was conducted using a non-invasive computerized system, which allowed for the automatic collection of facial landmarks and the subsequent reconstruction of three-dimensional coordinates. The sample comprised 269 children within the specified age range. Each child's facial features were captured using the non-invasive computerized system, which utilized two infrared CCD cameras, real-time hardware for label recognition, and software for three-dimensional landmark reconstruction. Sixteen cutaneous facial landmarks were automatically collected for each participant. From these landmarks, 10 angular and 15 linear measurements, as well as five direct distance rates, were derived. The mean values for each age class were calculated separately for children with bilateral Angle Class I occlusion and compared with those for children with bilateral Class II occlusion. In all children, the left and right occlusal classes were measured as suggested by Katz. The analysis revealed notable differences, primarily in the three-dimensional angular measurements between children with Class I and II occlusions. Specifically, Class II children exhibited more convex faces in the sagittal plane and a less prominent lower jaw compared to Class I children. However, no significant differences were observed in linear measurements, except for the lower facial height rate, which varied inconsistently across age groups between the two occlusion types. the findings of this research highlight distinct three-dimensional facial morphological differences between children with Class I and II occlusions. While Class II children tended to have more convex facial profiles and less prominent lower jaws, linear measurements showed minimal variation between the two occlusion types. These results underscore the importance of three-dimensional analysis in understanding facial morphology in growing patients with different occlusal patterns.

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

RP-Net: A Robust Polar Transformation Network for rotation-invariant face detection

Face detection is challenging in unconstrained environments, where it encounters various challenges such as orientation, pose, and occlusion. Deep convolutional neural networks, particularly cascaded ones, have greatly improved detection performance but still struggle with rotating objects due to limitations in the Cartesian coordinate system. Although data augmentation can mitigate this issue, it also increases computational demands. This paper introduces the Robust Polar Transformation Network (RP-Net) for rotation-invariant face detection. RP-Net converts the complex rotational problem into a simpler translational one to enhance feature extraction and computational efficiency. Additionally, the Advanced Spatial-Channel Restoration (ASCR) module optimizes facial landmark detection within polar domains and restores critical details lost during transformation. Experimental results on benchmark datasets show that RP-Net significantly improves rotation invariance over traditional CNNs and surpasses several state-of-the-art rotation-invariant face detection methods.

A lightweight network-based sign language robot with facial mirroring and speech system

Human–robot interaction is an essential capability for humanoid robots to enter the physical world and become companions in people’s lives, learning, and work. While the majority of current research focuses on the voice-based interactions of robots, yet over 60% of communication occurs through nonverbal behaviors, such as facial expressions and hand gestures. Endowing robots with the ability to communicate through nonverbal behavior not only enhances the interactive experience with robots but also provides a potential communication tool for individuals with hearing or speech impairments. Here, we develop a humanoid robot capable of adjusting facial movements by driving servos, and design a novel framework for the robot to integrate sign language recognition and facial landmark detection algorithms. This framework facilitates the robot recognize sign language and translate it into spoken language, while also imitating the facial expressions of the signers. To achieve this, we also propose a lightweight deep learning network called RealTimeSignNet for real-time sign language recognition. Leveraging lightweight 3D convolution modules and time-dependent constraints, this model adapts to various time scales, ensuring efficient processing of sign language recognition tasks. Experimental results demonstrate the outstanding performance of the RealTimeSignNet model on mainstream sign language datasets, achieving an accuracy of 88.1% on the large continuous sign language dataset (continuous SLR), 98.2% on the isolated sign language dataset (SLR 500), and 91.50% on the English sign language dataset (WLAS). The overall assessment demonstrates that our humanoid robot is capable of recognizing sign language and translating it into spoken language, while imitating the facial emotions, providing a comprehensive solution to the communication challenges faced by individuals with hearing and speech impairments.

3D evaluation of sagittal inclination of the maxillary dentition in relation to facial landmarks: A cohort study.

The determination of the maxillary occlusal plane presents a significant clinical challenge in the treatment of edentulous patients as well as it is critical for complex full-mouth reconstructions in dentate patients, including those with implant-supported rehabilitations. While the use of a Fox plane plate is standard in edentulous cases, its application in dentate patients lacks thorough documentation in existing literature. This clinical study assessed the sagittal position of the maxillary dentition in relation to facial landmarks using a digital three-dimensional analysis and evaluated the suitability and reliability of applying a simulated Fox plane plate, also known as an occlusal plane guide, in dentate patients. Eighty-one subjects were recruited at the Department of Prosthetic Dentistry of Goethe University Frankfurt, Germany, according to specific inclusion criteria. Intraoral and facial scans were obtained and analyzed using GOM Inspect Pro software (GOM, Braunschweig, Germany). The angles between the maxillary occlusal plane and three variations each of Camper's plane and ala-tragus line, relating to superior, middle, and inferior tragus points, were measured. These modified planes were then compared to a plane established by a simulated digital Fox plane plate, which was adapted to the maxillary anterior teeth and the lowest point of the posterior teeth in both quadrants. A total of 81 subjects (58 female and 23 male) with a mean age of 23.9 years were evaluated in this study. No significant angular difference was found between the angles of the maxillary occlusal plane compared with superior Camper's plane, middle Camper's plane, or superior ala-tragus line (p >0.05). The smallest angle occurred between superior Camper's plane and the maxillary occlusal plane on both the right (3.443°) and left (3.535°) sides. The application of a Fox plane plate resulted in two different occlusal planes in 70% of patients, significantly deviating from the digitally determined plane (p <0.05). Superior and middle Camper's planes, along with superior ala-tragus line, can be considered approximately parallel reference planes and are suitable for routine determining of the maxillary occlusal plane in restorative treatments. However, in contrast to digital evaluation methods, the application of a Fox plane plate in dentate patients showed high variability, indicating its low reproducibility due to its ambiguous positioning on the maxillary dentition. Clinical trial registration site: https://drks.de/search/de/trial/DRKS00030166.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

DeepFaceReshaping: Interactive deep face reshaping via landmark manipulation

Deep generative models allow the synthesis of realistic human faces from freehand sketches or semantic maps. However, although they are flexible, sketches and semantic maps provide too much freedom for manipulation, and thus, are not easy for novice users to control. In this study, we present DeepFaceReshaping, a novel landmark-based deep generative framework for interactive face reshaping. To edit the shape of a face realistically by manipulating a small number of face landmarks, we employ neural shape deformation to reshape individual face components. Furthermore, we propose a novel Transformer-based partial refinement network to synthesize the reshaped face components conditioned on the edited landmarks, and fuse the components to generate the entire face using a local-to-global approach. In this manner, we limit possible reshaping effects within a feasible component-based face space. Thus, our interface is intuitive even for novice users, as confirmed by a user study. Our experiments demonstrate that our method outperforms traditional warping-based approaches and recent deep generative techniques.

Eye Based Interactive Communication System

Eye-based communication systems, like the Blink-To-Speak language, are essential tools for individuals with motor neuron disorders, enabling them to articulate their needs and emotions. These systems leverage eye movements as a means of interaction, providing a vital communication channel for those with severe speech impairments. By interpreting specific eye gestures, users can effectively convey their thoughts, fostering a sense of independence and improving their quality of life. Many of the existing eye-tracking technologies tend to be complex and costly, limiting their accessibility in low-income regions. Using a mobile phone camera, the system captures real-time video frames to detect and track the user’s eyes, leveraging advanced facial landmark detection. Four primary eye movements—Left, Right, Up, and Blink—form the basis of the Blink-To-Live communication model, allowing users to communicate a range of more than 60 daily commands. By translating sequences of three eye gestures, the system generates readable sentences that are displayed on a screen and can be vocalized through synthesized speech. Unlike other sensor-dependent systems, Blink-To-Live is designed for affordability, flexibility, and ease of use, making it accessible without the need for specialized software or hardware. A prototype has been tested on a range of participants, showing positive results in terms of ease of use, flexibility, and cost-effectiveness

Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks.

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover's Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times.

Influence of Head Circumference on the Accuracy of Facial Scanning: An In vitro Study

ObjectivesThis study aimed to investigate the impact of head circumference on the accuracy of three-dimensional (3D) facial scans, focusing on trueness and precision across three mannequin heads of different sizes. Material and methodsThree 3D-printed mannequin heads with circumferences of 30, 50, and 65 cm were used. Ten facial landmarks were identified to measure seven interlandmark distances and two angles. Direct anthropometric measurement, serving as the reference value, was taken using a digital vernier calliper for linear distance, and angle was calculated using the law of cosines. Each head was scanned six times using two systems: a dual-structured light facial scanner (iTom) and a stereophotogrammetry system (3dMD). Digital measurements were analysed using Meshlab and Blender for distances and angles, respectively. Trueness values were determined by comparing measurements to reference measurements, while precision values were derived from the variability among the six scans. Statistical analysis was performed using the Kruskal–Wallis test due to nonhomogeneous variances, followed by Bonferroni correction for pairwise group comparisons. ResultsFor each scanning system, overall deviations in trueness and precision significantly increased with head circumference. Five of the seven distances and one angle showed significant compromises in trueness with larger head circumferences. Most measurements exhibited significant precision decreasing due to head circumference changes, except for N-Pn and Pn-Sn. Additionally, 3dMD displayed higher overall trueness compared to iTom, with five out of seven linear measurements and one angular measurement showing better results. ConclusionHead circumference significantly affects both trueness and precision of 3D facial scans for both technologies, suggesting that facial imaging should be used with caution for larger faces. Selecting an appropriate scanning system, such as 3dMD, can help mitigate the negative effects of scanning larger objects.

Landmark-in-facial-component: Towards occlusion-robust facial landmark localization

Despite great efforts in recent years to research robust facial landmark localization methods, occlusion remains a challenge. To tackle this challenge, we propose a model called the Landmark-in-Facial-Component Network (LFCNet). Unlike mainstream models that focus on boundary information, LFCNet utilizes the strong structural constraints inherent in facial anatomy to address occlusion. Specifically, two key modules are designed, a component localization module and an offset localization module. After grouping landmarks based on facial components, the component localization module accomplishes coarse localization of facial components. Offset localization module performs fine localization of landmarks based on the coarse localization results, which can also be seen as delineating the shape of facial components. These two modules form a coarse-to-fine localization pipeline and can also enable LFCNet to better learn the shape constraint of human faces, thereby enhancing LFCNet's robustness to occlusion. LFCNet achieves 4.82% normalized mean error on occlusion subset of WFLW dataset and 6.33% normalized mean error on Masked 300W dataset. The results demonstrate that LFCNet achieves excellent performance in comparison to state-of-the-art methods, especially on occlusion datasets.

FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures - all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.

Human-AI collaboration improves adults’ oral biomechanical functions: A multi-centre, self-controlled clinical trial

ObjectivesMaintenance of oral muscle functions is important for survival and communication. Utilizing Artificial Intelligence (AI) as a self-health-management material has shown promise. Here we developed a functional and AI-enabled smartphone e-Oral APP that provided real-time feedback features to let humans collaborate with AI, targeting to improve oral biomechanical and speech functions of adults. MethodsA total of 113 neurologically healthy Hong Kong citizens over 50 years old were recruited in this study from June to October 2022. A set of 12 exercises of the tongue, lips, and jaw were conducted with guidance provided in the self-developed e-Oral APP, in which visual-audio demonstrations and immediate feedback on facial movements by an open-source AI-pretrained 68-point Ensemble of Regression Tree (ERT) face landmark detector was given to the users. After 8-week exercises, assessments after 1-week and 4-week post-training were conducted. Primary outcomes were measured on oral functions, including occlusal force, masticatory efficiency, tongue strength, along with dry mouth condition, and oral health-related quality of life (OHQoL), while secondary outcomes were comprised of oral diadochokinesis (DDK) and swallowing ability. The usability of the APP was evaluated by a self-written questionnaire. ResultsAfter exercising a set of 12 exercises on the tongue, lips, and jaw for 8 weeks, 70 out of 113 participants (average age of 67.70±4.93 y.o., 52 female and 18 male) had a significant improvement in the occlusal force (p < 0.001), while masticatory efficiency (p = 0.002), tongue pressure (p < 0.001) and endurance (p = 0.004) were also improved. 80.3 % of respondents rated the APP with an overall rating of 3 out of 5 points, and 68.6 % would recommend the APP to others. ConclusionsAI-based APP can be an effective approach to help healthy adults improve their occlusal force, masticatory efficiency, tongue functions, and oral diadochokinesis after 8-week home oral exercises. Furthermore, these improvements can be sustained for at least four weeks. Clinical significanceThis is the first AI-assisted APP developed for oral muscle training. Our findings demonstrated that a self-administrated human-AI collaboration APP can improve clinically oral muscle biomechanics and functions for healthy adults. AI technology in smartphone gadgets provides a cost-effective, convenient, and reliable means for oral muscles training for adults.

Impression management in the boardroom: How CEO facial trustworthiness influences turnover risk

This paper examines the effect of perceived trustworthiness on the turnover of Chief Executive Officers (CEOs) and its moderating role in the relationship between firm performance and CEO turnover. We employ a unique dataset comprising headshots of male CEOs in US-listed firms from 2010 to 2019 and a machine learning-based facial landmark detector to construct a composite facial trustworthiness index. Our results show that CEOs exhibiting high facial trustworthiness experience a lower turnover risk. We also find that facial trustworthiness strongly influences the link between firm performance and CEO turnover. Well-performing executives have a lower risk of being dismissed from their positions, and the dismissal risk is even lower among CEOs perceived as having a high facial trustworthiness. Conversely, underperforming CEOs are more likely to be dismissed, and the dismissal probability is greater among CEOs who have been perceived as being highly trustworthy. Our findings are consistent with the predictions of the expectancy violation theory, which posits that trustworthy-looking executives are more likely to be punished if they do not live up to the expectations held by the board of directors.

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.