Instrument analysis of the effect of project-based learning models on learning outcomes on the basic competencies of commercial hair trimming

This study was used to determine the feasibility of the instruments before being used for further research. This study calculates the results of the validation of three validators who are experts in their fields using arithmetic averages, the validated instruments are: (1) the syllabus gets a score of 92.67%; (2) Learning Implementation Plan (RPP) scored 94.4%; (3) The Knowledge Assessment Instrument received a score of 93.36%; (4) The skill assessment instrument got a score of 93.31%; (5) The attitude assessment instrument got a score of 96.89%; (6) The Project Based Learning module received a score of 93.1%. In addition, this study also calculates the validity of the items, the differentiability of the items, the level of difficulty of the items and the reliability test of the items. This question consists of 30 items that have been tested on 15 students to find out the results of the calculation: (7) the validity of the items obtained a significance level value (ɑ) 0.05; (8) the level of difficulty of the items obtained a percentage of 16.67 for the easy question category, 70% for the medium question category, and 13.33% for the difficult question category; (9) The differentiating power of the items has very good criteria; and (10) the reliability of the items got a score of 0.753. From the calculation results that have been mentioned, it can be stated that the instrument of the influence of project-based learning models on learning outcomes on the basic competencies of commercial hair trimming is declared feasible and can be used for further research

Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah

Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik dari pada metode sebelumnya. Makalah ini mencoba menyajikan perbandingan kinerja klasifikasi wajah dengan empat arsitektur deep learning Xception, ResNet50, InceptionResNet-v2, Inception-v3. Dataset yang digunakan berjumlah 4500 gambar yang terbagi lima kelas heart, long, oblong, square, round. Berbagai pengoptimal deep learning diantaranya; transfer learning, optimizer deep learning, dropout dan fungsi aktivasi diterapkan untuk meningkatkan kinerja model. Perbandingan antara berbagai model CNN didasarkan kinerja metrik seperti accuracy, recall, precision dan F1-score. Dengan demikian dapat disimpulkan bahwa model Inception-ResNet-V2 menggunakan fungsi aktivasi Mish dan optimizer Nadam mencapai nilai tertinggi dengan accuracy dan f1-score masing-masing 92.00%, dan penggunaan waktu 65.0 menit. AbstractCurrently, face shape classification is widely applied in various fields. In the fashion industry, it can be used for hairstyle selection, eyeglass frame selection, makeup, and other modes. In the medical field, the face shape is used for plastic surgery. Identification of face shape is a challenging task due to the complexity of the face, size, lighting, age and expression. Many methods have been developed to provide the best accuracy results in the classification of face shapes. Deep learning is becoming a trend in the field of computer vision because it gives the best results than the previous method. This paper attempts to present a comparison of the performance of face classification with four deep learning architectures Xception, ResNet50, InceptionResNet-v2, Inception-v3. The dataset used is 4500 images divided into five classes heart, long, oblong, square, round. Various deep learning optimizers include; transfer learning, deep learning optimizer, dropout and activation functions are implemented to improve model performance. Comparisons between various CNN models are based on performance metrics such as accuracy, recall, precision and F1-score. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes.

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.</jats:p

Investigating facial shape using multilevel principal component analysis

SUMMARY Aims: 1. To determine the influence of geographical location, sex, height, Body Mass Index (BMI), age (14-16 years old), pubertal stage, metabolic factors, atopy, breathing disorders, maternal smoking and alcohol consumption during pregnancy on facial shape. 2. To explore the usefulness of Multilevel Principal Component Analysis (mPCA) in facial shape research. Method: The influence of geographical location and sex was assessed using 21 landmarks on 3D facial scans of subjects from Croatia (n=73), England (n=79), Wales (n=50) and Finland (n=47). The influence of sex, height, BMI, age (14-16 years old), pubertal stage, metabolic factors, atopy, breathing disorders, maternal smoking and alcohol consumption during pregnancy on adolescent facial shape was assessed using 1000 and 7160 quasi-landmarks on 3D facial scans of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n=1411). The results of mPCA were compared to those using landmarks only, conventional Principal Component Analysis (PCA), Discriminate Function Analysis (DFA) and Partial Least Squares Regression (PLSR). mPCA was also assessed as a variable selection tool prior to PLSR. Results: mPCA provided more meaningful information in the exploratory phase of data analysis than conventional PCA and DFA. However, the results must be interpreted with caution when group sizes are imbalanced. All variables reached significance, except for age, in their respective mPCA models. Geographical location, sex, height, BMI and fasting insulin explained greater than 5% of the total variation. These variables also reached significance in the PLSR models. Therefore 5% may be a useful threshold for PLSR variable selection. Conclusions: Sex, geographical location, height, BMI and fasting insulin had the most influence on facial shape. mPCA appears to be a useful tool for visualising the maximum variation between groups of subjects when group sizes are balanced and as a variable selection tool to inform more sophisticated models such as PLSR