Business Cases

• Face image recognition system - algorithm R&D

  We developed Face Image Recognition System with cameras placed in real environments.

  Roughly speaking, face recognition systems are classified in two application form: "Validation" and "Identification". Validation is used for users to identify themselves in front of door access control system. Meanwhile, identification is used for estimating identity of unspecified person from candidate list in such a system as urban monitoring.

  Identification results are computed based on face image of unspecified person and validation results of the face image in candidate list It is said that identification is more difficult in general than validation. For example, because of dispersion of validation scores of each person and face feature changes by disturbances, identification results sometimes contain rejection of identical person or misidentification of person.

  In this work, to improve identification accuracy, we implemented validation score normalization algorithm in C++. Also we implemented tuning tools in MATLAB which choose learning data tolerant of disturbances and image processing parameters.

  

  Technologies

  • Biometrics
  • Image Processing
  • Pattern Recognition
  • Statistical Analysis
  • MATLAB
  • C++

• Change point detection of time series - technical survey

  Surveyed theories and technologies of detecting change points on a given time series in some meaning and summarized as a report document.

  These technologies are generally called "change point detection", "anomaly detection", or "motif discovery", and many methods have been developed. The fields in need of these technologies are wide, not only signal processing but also security system, bioinformatics, multimedia processing and so on.

  Mainly surveyed the following methods.

  • Symbolic Aggregation Approximation Method
  • Random Projection Pursuit Method
  • Markov-Suffix Tree Method
  • Normalized Edit Distance Method
  • Isomap Nonlinear Dimension Reduction Method
  • Kolmogorov Complexity Anomaly Detection Method
  • Motif Discovery by PCA and MDL Principal

• Validation of the accuracy of a real-time situation assessment model using time-series sensor data - application development

  We have developed an application that analyzes time-series data measured by physical sensors, estimates a prediction model to determine the situation in real time, and verifies the accuracy of the prediction.

  Situation judgments were made using binary values. To learn to judge the situation, we set a threshold value for the frequency distribution of each sensor data and developed a annotation support application to classify positive and negative cases.

  We estimated two types of situation judgment prediction models: a logistic regression model and an SVM learning model. In order to verify the prediction accuracy of the situation judgment prediction model, we developed an application that synchronizes sensor data with corresponding video data, and visually verified the prediction accuracy in real time.

  Technologies

  • R
  • C++/CLI
  • Feature Selection
  • Logistic Regression
  • SVM
  • Cross Validation

See more cases