Embedding Aggregation for Forensic Facial Comparison
This work addresses the challenge of unreliable facial evidence in legal contexts, particularly for low-quality images from uncontrolled environments, though it is incremental as it builds on existing embedding methods.
The paper tackles the problem of poor-quality facial images in forensic facial comparison by aggregating deep neural network embeddings from multiple images of the same person, resulting in significant performance improvements such as a 95% reduction in Cllr for CCTV images and 96% for social media images.
In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal matters. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve performance in facial verification. We observe significant performance improvements, especially for very low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of score-based likelihood ratio systems and report improvements in Cllr of up to 95% (from 0.249 to 0.012) for CCTV images and of up to 96% (from 0.083 to 0.003) for social media images.