Alizadeh Sayyad, Kose Cemal
Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey.
Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey.
Forensic Sci Int. 2017 Aug;277:103-114. doi: 10.1016/j.forsciint.2017.05.025. Epub 2017 Jun 8.
Shoe marks are regarded as remarkable clues which can be usually detected in crime scenes where forensic experts use them for investigating crimes and identifying the criminals. Hence, developing a robust method for matching shoeprints with one another is of critical significance which can be used for recognizing criminals. In this paper, a promising method is proposed for retrieving shoe marks by means of developing blocking sparse representation technique. In the proposed method, the queried image was divided into two blocks. Then, two sparse representations are extracted for each queried image through approximate ℓ minimizing method. Also, the referenced database is categorized into two parts and two separate dictionaries are developed via them. Next, using the blocks, the total errors of classes are measured by resetting the coefficients related to other classes into zero. The performance of the proposed method was evaluated via the following methods Wright's sparse representation, extracting shoeprint image local and global features by Fourier transform, extracting shoeprint image features by Gabor transform after the image is rotated and extracting the corners of shoeprint image by Hessian and Harris' multi-scale detectors and SIFT descriptors. Accurate detection score was obtained in terms of the ratio of the number of accurately detected images to the total test images. The results of simulations indicated that the proposed method was highly effective and efficient in retrieving shoe marks, whole shoeprints, partial toe and heel shoeprints. Furthermore, it was found that the proposed method had better performance than the other methods with which it was compared. Accurate identification rate according to cumulative match score for the first n matches was measured. That is to say, the proposed method accurately recognized 99.47% of whole shoeprints, 80.53% of partial toe shoeprints and 79.47% of partial heel shoeprints in the first rank. Also, the proposed method was compared with the other methods in terms of rotation and scale distortions. The results indicated that the proposed method was resistant to these distortions.
鞋印被视为重要线索，通常能在犯罪现场被发现，法医专家利用它们来调查犯罪和识别罪犯。因此，开发一种强大的鞋印相互匹配方法至关重要，可用于识别罪犯。本文提出了一种有前景的方法，通过开发分块稀疏表示技术来检索鞋印。在所提方法中，将查询图像分为两个块。然后，通过近似ℓ最小化方法为每个查询图像提取两个稀疏表示。此外，将参考数据库分为两部分，并通过它们开发两个单独的字典。接下来，利用这些块，通过将与其他类相关的系数重置为零来测量类的总误差。通过以下方法评估所提方法的性能：赖特的稀疏表示、通过傅里叶变换提取鞋印图像的局部和全局特征、在图像旋转后通过伽柏变换提取鞋印图像特征以及通过黑塞矩阵和哈里斯多尺度检测器及尺度不变特征变换描述符提取鞋印图像的角点。根据准确检测图像数量与总测试图像数量的比率获得准确检测分数。模拟结果表明，所提方法在检索鞋印、完整鞋印、部分脚趾和脚跟鞋印方面非常有效。此外，发现所提方法比与之比较的其他方法具有更好的性能。测量了根据前n次匹配的累积匹配分数的准确识别率。也就是说，所提方法在第一等级中准确识别了99.47%的完整鞋印、80.53%的部分脚趾鞋印和79.47%的部分脚跟鞋印。此外，在所提方法与其他方法在旋转和尺度失真方面进行了比较。结果表明，所提方法对这些失真具有抗性。
