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.

Production of Agglomerates, Composite Materials, and Seed Coatings from Tannery Waste as New Methods for Its Management

This paper presents methods for managing waste produced by the leather industry, including tanning shavings derived from chrome tanning technologies and collagen preparations. Shavings were classified according to their shape (in accordance with Zingg′s shape classification). The content of individual elements was determined, together with the content of volatile organic compounds. Two new products were developed as part of the completed works: agglomerates (methods of non-pressure granulation) and composite materials were produced on the basis of tanning shavings and mineral fillers. Young′s modulus values classify these composite materials in the group of polymers and certain materials from the group of elastomers. A method for seed coating (on the example of legumes and rape) was also developed using a disc granulator, including collagen preparations in one of the layers as a solution for preventing the effects of droughts (biostimulant). The analyses of selected properties of the new products confirm the wide possible application of waste shavings and collagen preparations in a circular economy, especially in the construction, packaging, and agricultural sectors.

Forensic science and fractal nature analysis

Forensic photography, also referred to as crime scene photography, is an activity that records the initial appearance of the crime scene and physical evidence in order to provide a permanent record for the court. Nowadays, we cannot imagine a crime scene investigation without photographic evidence. Crime or accident scene photographs can often be reanalyzed in cold cases or when the images need to be enlarged to show critical details. Fractals are rough or fragmented geometric shapes that can be subdivided into parts, each of which is a reduced copy of the whole. Fractal dimension (FD) is an important fractal geometry feature. There are many applications of fractals in various forensic fields, including image processing, image analysis, texture segmentation, shape classification, and identifying the image features such as roughness and smoothness of an image. Fractal analysis is applicable in forensic archeology and paleontology, as well. The damaged image can be reviewed, analyzed, and reconstructed by fractal nature analysis.

Selection of breast features for young women in northwestern China based on the random forest algorithm

In the research of breast morphology, numerous breast features are measured, whereas only a few parameters are adopted for classification. Therefore, how to extract the key variables from the multi-dimensional features in a rational way is an issue that is focused upon. This study aimed to reduce the complexity of the dimensionality reduction for further improving the objectivity and interpretability of the selected breast features. Since the random forest (RF) algorithm can quantify the feature importance during training, the method was adopted to determine the optimal breast features for classification and recognition in this paper. Firstly, the anthropometric data of 360 females from northwestern China aged from 19 to 27 years were measured by non-contact three-dimensional body scanning technology and the contact manual measurement method. Then, the k-means clustering was applied to categorize breast shapes, and the RF algorithm was utilized to quantify and rank the importance of 25 breast features. Finally, to verify the availability of the RF algorithm on breast feature selection, the t-distributed stochastic neighbor embedding method was adopted to visualize the distribution of breast shape clusters into two dimensions. Meanwhile, four neural networks were determined to recognize the breast morphology. The results demonstrate that fewer breast features can effectively increase the accuracy of breast shape classification and recognition. The best performance of breast shape classification and recognition is obtained when the number of breast features is 13. In this case, the average Hamming loss of four neural networks is the smallest (0.1136). Interestingly, the bust circumference and the horizontal curve of breasts across the bust points are found to be the most important of the 25 breast features in this paper. The importance of the breast curve features is higher than that of the breast cross-sectional features, while the breast positioning features have the lowest importance. Meanwhile, the RF algorithm is verified to be more effective than traditional dimensionality reduction methods, such as principal component analysis, hierarchical clustering, and recursive feature elimination. The approach developed in this paper can be generalized to the dimensionality reduction of other body morphology.