{"slug": "sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and", "title": "Sex Estimation from Footwear Outsole Impressions Using CNN Transfer Learning and Interpretable Image Statistics", "summary": "A study posted to arXiv (2609.25386v1) found that fine-tuned convolutional neural networks outperformed traditional feature-based classifiers for binary sex estimation from footwear outsole impressions, using a publicly available dataset with a shoe-level train/test partition that keeps replicate scans of the same physical shoe together to reduce data leakage. The researchers compared end-to-end fine-tuning, frozen feature extraction with support vector machine classification, and hybrid feature fusion of handcrafted, geometric, and metadata-derived descriptors, finding that fine-tuned CNNs achieved the strongest overall predictive performance while frozen-feature approaches offered a less computationally demanding alternative. Exploratory analysis linked low-dimensional CNN representations to frequency threshold ratio, image contrast, and wavelet-based summaries, though the authors state further validation on independently collected and casework-like impressions is needed before operational use.", "body_md": "arXiv:2609.25386v1 Announce Type: new \nAbstract: Footwear outsole impressions are a common form of forensic pattern evidence, yet quantitative methods for estimating wearer attributes from these images remain relatively underdeveloped. We investigate binary sex estimation from footwear outsole impressions by comparing convolutional neural network (CNN) transfer learning with traditional feature-based classification. Using a publicly available outsole-impression dataset, we adopt a shoe-level training and test partition that keeps replicate scans of the same physical shoe together to reduce data leakage. We evaluate pretrained CNNs through end-to-end fine-tuning, frozen feature extraction followed by support vector machine classification, and hybrid feature fusion incorporating handcrafted, geometric, and metadata-derived descriptors. Fine-tuned CNNs achieve the strongest overall predictive performance and substantially outperform traditional classifiers trained on the manually specified descriptors alone, while frozen-feature approaches offer a less computationally demanding alternative. Exploratory analysis of low-dimensional CNN representations reveals associations with frequency threshold ratio, image contrast, and wavelet-based summaries, providing a connection between learned representations and measurable properties of outsole impressions. These findings suggest that CNN transfer learning captures discriminative information beyond the descriptors considered and offers a promising approach to footwear-based forensic screening. Further validation on independently collected and casework-like impressions is needed before operational use.", "url": "https://wpnews.pro/news/sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and", "canonical_source": "https://arxiv.org/abs/2609.25386", "published_at": "2026-09-23 04:00:00+00:00", "updated_at": "2026-09-23 04:27:09.714830+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "neural-networks", "ai-research"], "entities": ["arXiv", "convolutional neural network", "support vector machine"], "alternates": {"html": "https://wpnews.pro/news/sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and", "markdown": "https://wpnews.pro/news/sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and.md", "text": "https://wpnews.pro/news/sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and.txt", "jsonld": "https://wpnews.pro/news/sex-estimation-from-footwear-outsole-impressions-using-cnn-transfer-learning-and.jsonld"}}