FaceAtt: Enhancing Image Captioning with Facial Attributes for Portrait
  Images
Naimul Haque Iffat Labiba Sadia Akter
Automated image caption generation is a critical area of research that
enhances accessibility and understanding of visual content for diverse
audiences. In this study, we propose the FaceAtt model, a novel approach to
attribute-focused image captioning that emphasizes the accurate depiction of
facial attributes within images. FaceAtt automatically detects and describes a
wide range of attributes, including emotions, expressions, pointed noses, fair
skin tones, hair textures, attractiveness, and approximate age ranges.
Leveraging deep learning techniques, we explore the impact of different image
feature extraction methods on caption quality and evaluate our model's
performance using metrics such as BLEU and METEOR. Our FaceAtt model leverages
annotated attributes of portraits as supplementary prior knowledge for our
portrait images before captioning. This innovative addition yields a subtle yet
discernible enhancement in the resulting scores, exemplifying the potency of
incorporating additional attribute vectors during training. Furthermore, our
research contributes to the broader discourse on ethical considerations in
automated captioning. This study sets the stage for future research in refining
attribute-focused captioning techniques, with a focus on enhancing linguistic
coherence, addressing biases, and accommodating diverse user needs.