Face Detection and Recognition: Theory and Practice by Asit Kumar Datta, Madhura Datta, Pradipta Kumar Banerjee

By Asit Kumar Datta, Madhura Datta, Pradipta Kumar Banerjee

Face detection and popularity are the nonintrusive biometrics of selection in lots of safety functions. Examples in their use contain border keep watch over, driver’s license issuance, legislation enforcement investigations, and actual entry control.

Face Detection and popularity: thought and perform

elaborates on and explains the idea and perform of face detection and popularity platforms presently fashionable. The e-book starts off with an creation to the cutting-edge, delivering a basic evaluate of the on hand equipment and a sign of destiny learn utilizing cognitive neurophysiology. The textual content then:

  • Explores subspace equipment for dimensionality aid in face photograph processing, statistical tools utilized to stand detection, and clever face detection tools ruled by way of synthetic neural networks
  • Covers face detection with color and infrared face photos, face detection in actual time, face detection and popularity utilizing set estimation conception, face popularity utilizing evolutionary algorithms, and face popularity in frequency domain
  • Discusses equipment for the localization of face landmarks important in face attractiveness, tools of producing artificial face photos utilizing set estimation idea, and databases of face photographs on hand for checking out and coaching systems
  • Features pictorial descriptions of each set of rules in addition to downloadable resource code (in MATLAB®/PYTHON) and implementation thoughts with code examples
  • Demonstrates how frequency area correlation options can be utilized delivering exhaustive try results

Face Detection and popularity: conception and perform provides scholars, researchers, and practitioners with a unmarried resource for state-of-the-art info at the significant methods, algorithms, and applied sciences utilized in automatic face detection and recognition.

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Additional resources for Face Detection and Recognition: Theory and Practice

Example text

Feature-based approaches for face detection . . . . . . . . . . . 1 Low-level analysis . . . . . . . . . . . . . . . . . . . . 1 Edges . . . . . . . . . . . . . . . . . . . . 2 Gray-level analysis . . . . . . . . . . . . . 3 Color information in face detection . . . . 4 Motion-based analysis . . . . . . . . . . . 2 Active shape model . . . . . . . . . . . . . . . . .

3 Neural network-based face recognition . . . . . . . . . 4 Correlation-based method . . . . . . . . . . . . . . . . 5 Matching pursuit-based methods . . . . . . . . . . . . 6 Support vector machine approach . . . . . . . . . . . 7 Selected works on face classifiers . . . . . . . . . . . . Face reconstruction techniques . . . . . . . . . . . . . . . . . . 1 Three-dimensional face recognition . . .

To extract features under uncertainty). However, the appearance-based recognition techniques have their own limitations in recognizing human faces in images with wide variations of head poses and illumination. In summary, external and internal facial components, distinctiveness, configuration and local texture of facial components all contribute to the process of face detection and recognition. In contrast, humans can seamlessly blend and independently perform appearance-based and geometry-based detection and recognition tasks most efficiently.

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