An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.

Relationship between Dental Arch Width and Vertical Facial Morphology in Untreated Adults- A Retrospective Study

Early orthodontic research has focused on the link between face shape and malocclusion. Orthodontic treatment's effectiveness and stability are heavily influenced by a patient's dental and facial anatomy. An orthodontist's knowledge of arch shapes is essential since it affects the patient's treatment and future growth. For this study, the researchers wanted to see if there was a link between vertical face morphology and arch width, and if there was a difference in arch width between males and females. Arch width measurements (in millimetres) were utilised to determine the association. For both males and females, participants with the lowest mandibular plane angle had the widest arch, followed by those with the average mandibular angle and those with the highest.

Automatic Face Shape Classification Via Facial Landmark Measurements

This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter.

CLINICAL IMPLICATIONS OF ANTHROPOMETRIC STUDY OF FACIAL INDEX IN FIRST YEAR MEDICAL STUDENTS OF SKIMS MEDICAL COLLEGE

Introduction:The structure of face is dependent on various attributes such as gender, ethnic background, race,climate , socioeconomic status and genetics. The facial index is the ratio of facial height (distance between Nasion and Gnathion) and maximum facial breadth(distance between two zygomatic prominences from Right zygonion to Left zygonion). Methods:A total of one hundred and twelve (112) First year medical students studying at Sheri Kashmir Institute of Medical Sciences Bemina Srinagar, Kashmir were chosen as the subjects for the present study. The study was carried out at Department of Anatomy, Sheri Kashmir Institute of Medical Sciences Bemina Srinagar Kashmir. Results:The conducted research provides important information concerning the total facial index (TFI), face shape, and facial phenotype in the adult population. The mean values of the morphological facial height and facial breadth in males were, respectively150mm and 155.5mm.while in females, respectively were: 142.5 mm and 170.0mm . Conclusion:Keeping in view the results obtained in this study we concluded that the dominant facial phenotype in the medical students of sheri Kashmir Institute of medical sciences is EuryproscopicandLeptoproscopic in male students and in male students.The results obtained may be harnessed as a reference for facial analysis that will be further useful in Orthodontics, anthropological research, forensics, genetic research and reconstructive surgery.

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.