{
  "id": "1807.01332",
  "version": "v1",
  "published": "2018-07-03T18:10:30.000Z",
  "updated": "2018-07-03T18:10:30.000Z",
  "title": "Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification",
  "authors": [
    "Sobhan Soleymani",
    "Ali Dabouei",
    "Hadi Kazemi",
    "Jeremy Dawson",
    "Nasser M. Nasrabadi"
  ],
  "comment": "Accepted in \"2018 International Conference on Pattern Recognition\"",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple feature abstraction levels. Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification. Features extracted at different convolutional layers of a modality-specific CNN represent the input at several different levels of abstract representations. We demonstrate that an efficient multimodal classification can be accomplished with a significant reduction in the number of network parameters by exploiting these multi-level abstract representations extracted from all the modality-specific CNNs. We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features from the last layer of each modality-specific CNNs. We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy. We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-03T18:10:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional neural networks",
      "multi-level feature abstraction",
      "multimodal biometric identification",
      "modality-specific cnn",
      "multimodal fusion algorithm consists"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}