AI-Personalized Elegance: Optimizing Your Appearance with Smart Hairstyle Recommendations

AI-Personalized Elegance: Optimizing Your Appearance with Smart Hairstyle  Recommendations

1Neha Patel, 2Nikita Prajapati, 3 Ritu Yadav, 4Sakshi Kumari,5 Dr.Preeti Gupta

1,2,3,4,5Department of Computer Science and Engineering

PRESTIGE INSTITUTE OF ENGINEERING MANAGEMENT AND RESEARCH ,INDORE

Abstract:
Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very impressive performances. In general, deep architecture models suffer from the over-fitting problem when there is a small number of training data. In this article the attempt is made to remedy this problem in deep architecture with regularization techniques including overlap pooling and flipped image augmentation and dropout; the authors also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. The authors present a hybrid neural-network solution which compares favorably with other methods.

Keyword
Hair style recommendation; Face recognition; Machine learning; Software Development