Landmark Extraction Research Articles

Analyzing Discrepancies and Correlations in Soft and Hard Tissue Asymmetry: A Focused Study on Hemifacial Microsomia and Isolated Microtia.

This study aims to thoroughly investigate the distinctions and relationships between facial hard and soft tissue asymmetry, as well as their variations within different conditions and age groups. This cross-sectional study analyzed pre-treatment computed tomography (CT) images from 120 male patients aged 5 to 12 years with unilateral HFM (Pruzansky-Kaban types I and IIA) or isolated microtia. The 120 patients were categorized into four groups by condition (HFM or isolated microtia) and age (5-7, 8-12 years). Segmentation and extraction of anatomical landmarks were performed using Mimics software, followed by generating root-mean-square (RMS) values for facial asymmetry. Statistical analysis, including the Wilcoxon signed-rank test and Spearman correlation coefficient, was conducted to assess differences and correlations in asymmetry. The study found that soft tissue asymmetry was generally greater than hard tissue asymmetry across all facial regions. The complexity of regional asymmetry correlations increased with age in HFM patients but decreased in isolated microtia patients. Significant correlations between hard and soft tissues were observed within the same anatomical regions, particularly in HFM patients aged 8-12 years. The intricate interplay between hard and soft tissues was observed in HFM and isolated microtia patients. Personalized analysis of the causes of asymmetry in different regions is crucial for the formulation of effective treatment plans. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

MULO: LiDAR Odometry via MUlti-Landmarks Joint Optimization

At the present stage, LiDAR-based SLAM solutions are dominated by ICP and its variants, while the BA optimization method that can improve the pose consistency has received little attention. Therefore, we propose MULO, a low-drift and robust LiDAR odometry using BA optimization with plane and cylinder landmarks. In the front-end, a coarse-to-fine direct pose estimation method provides the prior pose to the back-end. And in the back-end, we propose a novel three-stage landmark extraction and data association strategy for plane and cylinder, which is robust and efficient. Meanwhile, a stable minimum parameterization method for cylinder landmarks is proposed for optimization. In order to fully utilize the LiDAR information at long distances, we propose a new sliding window structure consisting of a TinyWindow and a SuperWindow. Finally, we jointly optimize the two kinds of landmarks and scan poses in this sliding window. The proposed system is evaluated on public dataset and our dataset, and experimental results show that our system is competitive compared with the state-of-the-art LiDAR odometrys.

Automatic Dress Size Detection for Online Shopping

This project addresses the challenge faced by online shoppers in accurately determining dress sizes, leading to dissatisfaction and high return rates. To mitigate this issue, the proposed system introduces an automated approach to dress size detection, integrating human detection and landmark extraction techniques using YOLOv7. The system aims to enhance the online shopping experience by providing shoppers with personalised dress size recommendations based on their body measurements. By leveraging the real-time object detection capabilities of YOLOv7, the system detects human bodies within input images, subsequently extracting key landmarks such as shoulder width, bust size, and waist circumference. These measurements are then utilised to estimate the most suitable dress size for the user, thus addressing the problem of ill-fitting purchases in online shopping. Evaluation of the system's performance involves accuracy metrics and usability testing to validate its effectiveness and reliability in automating dress size detection for online shoppers. Through this innovative solution, online retailers can significantly reduce return rates and enhance customer satisfaction, while shoppers can enjoy a more convenient and personalised shopping experience

Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients.

Scoliosis, characterized by spine deformity, is most common in adolescent idiopathic scoliosis (AIS). Manual Cobb angle measurement limitations underscore the need for automated tools. This study employed a vertebral landmark extraction method and Feedforward Neural Network (FNN) to predict scoliosis progression in 79 AIS patients. The novel intervertebral angles matrix format showcased results. The mean absolute error for the intervertebral angle progression was 1.5 degrees, while the Pearson correlation of the predicted Cobb angles was 0.86. The accuracy in classifying Cobb angles (<15°, 15-25°, 25-35°, 35-45°, >45°) was 0.85, with 0.65 sensitivity and 0.91 specificity. The FNN demonstrated superior accuracy, sensitivity, and specificity, aiding in tailored treatments for potential scoliosis progression. Addressing FNNs' over-fitting issue through strategies like "dropout" or regularization could further enhance their performance. This study presents a promising step towards automated scoliosis diagnosis and prognosis.

Deepfake Face Detection Using Machine Learning with LSTM

Deep fake videos, which employ artificial intelligence to manipulate and generate highly convincing fake content, have emerged as a significant threat to society, potentially undermining trust in visual media. Detecting these deceptive videos is outmost importance to combat the spread of misinformation and protect the integrity of digital media. In this study, we propose a novel approach for deep fake face video detection utilizing Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN). Our approach capitalizes on the temporal patterns and context within video sequences, harnessing the unique strengths of LSTM in capturing sequential information. We demonstrate the effectiveness of our methodology by training the LSTM network on a diverse dataset comprising both real and deep fake videos. The network’s ability to learn temporal dependencies and identify inconsistencies in facial expressions, eye movements, and other subtle cues allows it to distinguish between genuine and manipulated content. To further enhance the accuracy and robustness of our deep fake face detection system, we integrate pre-processing techniques for frame- level analysis, such as optical flow computation and facial landmarks extraction. Additionally, we employ a comprehensive ensemble of LSTM models and other machine learning algorithms to improve the overall detection performance. In our experiments, we evaluate the LSTM-based deep fake detection system on a large-scale dataset of both known and unseen deep fake videos, achieving high detection accuracy and low false positive rates. We also compare our approach with existing methods, demonstrating its superiority in terms of robustness and generalization. The results of this study signify the potential of LSTM-based models for mitigating the adverse effects of deep fake content on society. As deep fake technology continues to evolve, our approach showcases a promising step towards combating the dissemination of deceptive multimedia, promoting media integrity, and upholding trust in visual information. Keywords: LSTM Networks, Recurrent Neural Network, Optical flow computation, Facial landmarks extraction, False positive rates.

Automated Landmark Annotation for Morphometric Analysis of Distal Femur and Proximal Tibia.

Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee.

An Up-View Visual-Based Indoor Positioning Method via Deep Learning

Indoor positioning plays a crucial role in various domains. It is employed in various applications, such as navigation, asset tracking, and location-based services (LBS), in Global Navigation Satellite System (GNSS) denied or degraded areas. The visual-based positioning technique is a promising solution for high-accuracy indoor positioning. However, most visual positioning research uses the side-view perspective, which is susceptible to interferences and may cause concerns about privacy and public security. Therefore, this paper innovatively proposes an up-view visual-based indoor positioning algorithm. It uses the up-view images to realize indoor positioning. Firstly, we utilize a well-trained YOLO V7 model to realize landmark detection and gross extraction. Then, we use edge detection operators to realize the precision landmark extraction, obtaining the landmark pixel size. The target position is calculated based on the landmark detection and extraction results and the pre-labeled landmark sequence via the Similar Triangle Principle. Additionally, we also propose an inertial navigation system (INS)-based landmark matching method to match the landmark within an up-view image with a landmark in the pre-labeled landmark sequence. This is necessary for kinematic indoor positioning. Finally, we conduct static and kinematic experiments to verify the feasibility and performance of the up-view-based indoor positioning method. The results demonstrate that the up-view visual-based positioning is prospective and worthy of research.

Automatic Facial Palsy Detection—From Mathematical Modeling to Deep Learning

Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation. In this work, a detailed review of the quantification of facial palsy takes place, covering all methods ranging from traditional manual mathematical modeling to automated computer vision-based methods. Moreover, facial palsy quantification is defined in terms of facial asymmetry indices calculation for different image modalities. The aim is to introduce readers to the concept of mathematical modeling approaches for facial palsy detection and evaluation and present the process of the development of this separate application field over time. Facial landmark extraction, facial datasets, and palsy grading systems are included in this research. As a general conclusion, machine learning methods for the evaluation of facial palsy lead to limited performance due to the use of handcrafted features, combined with the scarcity of the available datasets. Deep learning methods allow the automatic learning of discriminative deep facial features, leading to comparatively higher performance accuracies. Datasets limitations, proposed solutions, and future research directions in the field are also presented.

Self-Organizing Memory Based on Adaptive Resonance Theory for Vision and Language Navigation

Vision and Language Navigation (VLN) is a task in which an agent needs to understand natural language instructions to reach the target location in a real-scene environment. To improve the model ability of long-horizon planning, emerging research focuses on extending the models with different types of memory structures, mainly including topological maps or a hidden state vector. However, the fixed-length hidden state vector is often insufficient to capture long-term temporal context. In comparison, topological maps have been shown to be beneficial for many robotic navigation tasks. Therefore, we focus on building a feasible and effective topological map representation and using it to improve the navigation performance and the generalization across seen and unseen environments. This paper presents a S elf-organizing Memory based on Adaptive Resonance Theory (SMART) module for incremental topological mapping and a framework for utilizing the SMART module to guide navigation. Based on fusion adaptive resonance theory networks, the SMART module can extract salient scenes from historical observations and build a topological map of the environmental layout. It provides a compact spatial representation and supports the discovery of novel shortcuts through inferences while being explainable in terms of cognitive science. Furthermore, given a language instruction and on top of the topological map, we propose a vision–language alignment framework for navigational decision-making. Notably, the framework utilizes three off-the-shelf pre-trained models to perform landmark extraction, node–landmark matching, and low-level controlling, without any fine-tuning on human-annotated datasets. We validate our approach using the Habitat simulator on VLN-CE tasks, which provides a photo-realistic environment for the embodied agent in continuous action space. The experimental results demonstrate that our approach achieves comparable performance to the supervised baseline.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology.Objective:To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology.Methods:We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists.We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models.Results:With annotators’ consensus as the ‘gold standard’, our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10−3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982].In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10−3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans.The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view.Conclusions:We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

Hand Gesture Controlled Presentation using OpenCV and MediaPipe

In today's digital era, presentations play a crucial role in various domains, ranging from education to business. However, traditional manual presentation methods, reliant on input devices such as keyboards or clickers, have inherent limitations in terms of mobility, interactivity, and user experience. To address these limitations, gesture-controlled presentations have emerged as a promising solution, harnessing the power of computer vision techniques to interpret hand gestures and enable natural interaction with presentation content. This paper presents a comprehensive system for hand gesture-controlled presentations using OpenCV and MediaPipe libraries. OpenCV is employed to capture video input from a webcam, while MediaPipe is utilized for hand tracking and landmark extraction. By analyzing finger positions and movements, the system accurately recognizes predefined gestures. Presenters can seamlessly control the slides, hold a pointer, annotate the content, and engage with the audience in a more interactive manner. The responsiveness and real-time performance contribute to an enhanced presentation experience.

Automatic extraction of upper body landmarks using Rhino and Grasshopper algorithms

The aim of this study is to develop algorithms for automatic landmark extraction on women with various upper body types and body inclinations using the Grasshopper algorithm editor, which enables the user to interact with the 3D modeling interface. First, 15 landmarks were defined based on the morphological features of 3D body surfaces and clothing applications, from which automatic landmark extraction algorithms were developed. To verify the accuracy of the algorithms on various body shapes, this study determined criteria for key body shape factors (BMI, neck slope, upper body slope, and shoulder slope) that influence each landmark position, classified them into body shape groups and sorted the scan samples for each body type using the 6th SizeKorea database. The statistical differences between the scan-derived measurements and the SizeKorea measurements were compared, with an allowable tolerance of ISO 20685. In the case of landmarks with significant differences, the algorithm was modified. It was found that the algorithms were successfully applied to various upper body shapes, which improved the reliability and accuracy of the algorithm.

Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology

Facial anthropometrics are measurements of human faces and are important figures that are used in many different fields, such as cosmetic surgery, protective gear design, reconstruction, etc. Therefore, the first procedure is to extract facial landmarks, then measurements are carried out by professional devices or based on experience. The aim of this review is to provide an update and review of 3D facial measurements, facial landmarks, and nasal reconstruction literature. The novel methods to detect facial landmarks including non-deep and deep learning are also introduced in this paper. Moreover, the nose is the most attractive part of the face, so nasal reconstruction or rhinoplasty is a matter of concern, and this is a significant challenge. The documents on the use of 3D printing technology as an aid in clinical diagnosis and during rhinoplasty surgery are also surveyed. Although scientific technology development with many algorithms for facial landmarks extraction have been proposed, their application in the medical field is still scarce. Connectivity between studies in different fields is a major challenge today; it opens up opportunities for the development of technology in healthcare. This review consists of the recent literature on 3D measurements, identification of landmarks, particularly in the medical field, and finally, nasal reconstruction technology. It is a helpful reference for researchers in these fields.

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

The 3D musculoskeletal motion of animals is of interest for various biological studies and can be derived from X-ray fluoroscopy acquisitions by means of image matching or manual landmark annotation and mapping. While the image matching method requires a robust similarity measure (intensity-based) or an expensive computation (tomographic reconstruction-based), the manual annotation method depends on the experience of operators. In this paper, we tackle these challenges by a strategic approach that consists of two building blocks: an automated 3D landmark extraction technique and a deep neural network for 2D landmarks detection. For 3D landmark extraction, we propose a technique based on the shortest voxel coordinate variance to extract the 3D landmarks from the 3D tomographic reconstruction of an object. For 2D landmark detection, we propose a customized ResNet18-based neural network, BoneNet, to automatically detect geometrical landmarks on X-ray fluoroscopy images. With a deeper network architecture in comparison to the original ResNet18 model, BoneNet can extract and propagate feature vectors for accurate 2D landmark inference. The 3D poses of the animal are then reconstructed by aligning the extracted 2D landmarks from X-ray radiographs and the corresponding 3D landmarks in a 3D object reference model. Our proposed method is validated on X-ray images, simulated from a real piglet hindlimb 3D computed tomography scan and does not require manual annotation of landmark positions. The simulation results show that BoneNet is able to accurately detect the 2D landmarks in simulated, noisy 2D X-ray images, resulting in promising rigid and articulated parameter estimations.

Generating Training Data Using Python Scripts for Automatic Extraction of Landmarks from Tooth Models

The automatic extraction of landmarks from three-dimensional tooth models using artificial intelligence is crucial to advance dental anatomy studies. However, collecting sufficient data for artificial intelligence training hinders progress in this field. To automatically identify anatomical landmarks on tooth models, this paper proposes a method for generating substantial amount of training data from the digital data of a single human maxillary canine. Human maxillary canine data were loaded into Blender, and the landmark was defined as the cusp of the canine. Sequentially, the coordinate values of the centroid of the landmark were used as correct answer labels. A total of 22,896 training images were generated using Python scripts, and they were split into train and validation datasets. Pairs of images and label data were fed into the artificial intelligence network, which comprises four convolutional layers and one max pooling layer. The accuracy of the trained artificial intelligence was evaluated on 915 200 × 200 pixel images, which were different from the training images. The average Euclidean distance error of the artificial intelligence-predicted coordinate values of the landmark was 6.1 pixels. Our approach, based on the training data generated in Blender using Python scripts, provides a powerful solution when it is challenging to obtain sufficient amount of medical field data for artificial intelligence-assisted procedures.

An Enhanced Face Detection System using A Novel FIS-CDNN Classifier

A computer application that can detect, track, identify or verify human faces as of an image or video capture utilizing a digital camera is Face Recognition (FR). Few challenges like low-resolution images, aging, uncontrolled pose, illumination changes, and poor lighting conditions are not tackled even though huge advancement has been created in the Face Detection and Recognition (FDR) domain. Utilizing the Modified Tiny Face Detection (MTFD) and Fuzzy Interference System - Convolutional Deep Neural Network (FIS-CDNN) classifier, a Face Recognition System (FRS) was proposed here to tackle all complications. Primarily, Gamma correction - Based Histogram Equalization (GBHE) technique is utilized to augment the image’s input in the pre-processing phase. The MTFD was employed to detect the face. Following that, the features are extracted. The Improved Chehra (IC) landmark extraction method was employed to retrieve the landmark features. And finally, the Geometric Features (GFs) are extracted. Later, the Gaussian - centered Spider Monkey Optimization (GSMO) Algorithm was employed to choose the vital features. To recognizing the face, the chosen features are fed into the FIS-CDNN classifier. When analogized to the prevailing models, it is concluded via the experiential outcomes that higher accuracy was attained by the proposed method.

Automatic extraction of vertebral landmarks from ultrasound images

Some forms of myopathies such as Duchenne muscular dystrophy cause a progressive degeneration of the patient's muscles. This results in the development of scoliosis, which increases in severity over time. The clinical standard for monitoring scoliosis is to perform anX-ray on a regular basis. Unfortunately, repeated exposure to X-rays is harmful to the patient's health. Ultrasound imaging is a radiation-free modality that uses ultrasound (US) waves. However, the interpretation of vertebral ultrasound images is often difficult due to the variable quality of the image. In order to tackle this challenge, we present a method to localize the vertebrae on US images automatically. The validation of this reproducible approach suggests that it would be possible, in the long term, to replace part of the X-ray exams by US imaging.

A random forest classifier with cost-sensitive learning to extract urban landmarks from an imbalanced dataset

ABSTRACT Urban landmarks play an important role as spatial references in spatial cognition, navigation, map design and urban planning. However, the current landmark extraction methods do not consider the imbalance between the landmark and non-landmarknon-landmark samples in a dataset, so the extraction results are biased toward the class with the majority of sample data, resulting in poor classification performance for the class with the fewest sample data. This study introduces a random forest (RF) classifier combined with cost-sensitive learning to extract urban landmarks automatically from a basic spatial database. First, the optimal feature set is determined according to the importance of features. Next, a cost-sensitive RF algorithm is applied to extract landmarks, which determines the misclassification cost according to the class distribution, and each decision tree is weighted by the classification results. The method has good performance, with a recall and area under the ROC curve (AUC) greater than 90%, and the model is also applicable to small sample sets, which can reduce the cost of manual labor.

Digital morphometrics: Application of MorphoLeaf in shape visualization and species delimitation, using Cucurbitaceae leaves as a model.

PremisePlant leaves are one of the most important organs for plant identification due to their variability across different taxonomic groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology‐based species delimitation, image‐analysis digital solutions have made it easy to detect more characters to complement existing leaf data sets.MethodsHere, we apply MorphoLeaf to generate a morphometric data set from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, the reparameterization of leaf contours, and data quantification and normalization. A statistical analysis was performed on the resulting data set.ResultsA principal component analysis revealed that leaf blade area, blade perimeter, tooth area, tooth perimeter, the measure of the distance from tooth position to the tip, and the measure of the distance from tooth position to the base are important and informative landmarks that contribute to the variation within the species studied.DiscussionMorphoLeaf can be applied to quantitatively track leaf diversity, thereby functionally integrating morphometrics and shape visualization into the digital identification of plants. The success of digital morphometrics in leaf outline analyses presents researchers with opportunities to carry out more accurate image‐based research in areas such as plant development, evolution, and phenotyping.

Estimation of sex through morphometric landmark indices in facial images with strength of evidence in logistic regression analysis

We report an investigation into the application of the logistic regression classifier for estimation of sex in facial images. We used 2000 images, 1000 each of both sexes from a publicly available database and automatically detected facial landmarks and derived some morphometric facial indices. These indices were used as predictors for the classification. As the traditional manual extraction of facial landmarks is time consuming, automatic detection of the landmarks improves the efficiency. The logistic regression classification is also compared with two other classification methods, the likelihood-ratio (LR) based method where the features of a face are evaluated in terms of the probability distribution of these features in both the sexes, and the Convolutional Neural Networks (CNN) methods. While is former is desirable from the viewpoint of interpretability and to assess the strength of evidence, the latter is sophisticated. We report an AUC of 0.94 with true positive (TP) rate of 88.4% for males and 87.9% for females for logistic regression-based classification. This performance is better than the likelihood ratio classifier with TP rate of 79.6% for males and 82.2% for females. The overall performance of logistics regression is slightly less than the CNN classifier that has 89.3% TP rate for males and 92.6% for females. We have extended these models to a CCTV image database, more representative of the forensic scenario and found the logistic regression performing better than the CNN method on an average for 8 different types of cameras. We conclude that as a trade-off between simplicity and sophistication, the logistic regression classifier can be used for a two-class problem like classification of sex from facial morphometric indices, and that the likelihood ratio approach can assess the strength of the classification, in conformance with the requirements of evidence interpretation.