• Akhila Menon

The fusion of deep learning and image processing has unlocked new opportunities for creating personalized images and altering artistic styles.This research presents Image Neural Transformation, a system that enables users to generate customized portraits by inputting specific parameters such as age, gender, and emotion. Sentiment analysis is used to understand the emotional context, enabling the creation of facial expressions that match the desired mood. In addition to generating personalized portraits, the system incorporates artistic style transfer techniques, enabling users to apply various artistic styles to the generated images. This dual approach not only provides a creative platform for users but also demonstrates the potential of neural networks in combining generative and stylistic capabilities. The paper details the architecture of the system, including data preprocessing, model training, and the integration of sentiment analysis for emotion-driven image generation. The results demonstrate the effectiveness of the system in producing high- quality images that reflect the specified characteristics, as well as the flexibility in applying diverse artistic styles. This research contributes to the growing field of AI- driven creativity, offering insights into the practical applications of neural networks in personalized content creation.

Generative Adversarial Networks, Artistic Style Transfer, Sentiment Anal- ysis, Deep Learning, Image Generation, Personalized Content Creation

"Image Neural Transformation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d168-d171, September-2024, Available :http://www.jetir.org/papers/JETIR2409320.pdf

"Image Neural Transformation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd168-d171, September-2024, Available at : http://www.jetir.org/papers/JETIR2409320.pdf