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1. Technical Specification 2. Conformance Testing 3. Performance Assessment

1. Technical Specifications

Table 1 provides the links to the specifications and the JSON syntax of all AIMs specified by Technical Specification: Portable Avatar Format (MPAI-PAF) V1.3. MPAI-PAF V1.3 AI-Modules supersede those previously specified which may still be used if their version is by explicitly signaled. AIMs in bold are Composite.

Table 1 – Specifications and JSON syntax of AIMs used by MPAI-PAF V1.3

Acronym AIM Name JSON Acronym AIM Name JSON
PAF-AVC Audio-Visual Scene Creation X PAF-PDX Portable Avatar Demultiplexing X
PAF-AVR Audio-Visual Scene Rendering X PAF-PMX Portable Avatar Multiplexing X
PAF-EBD Entity Body Description X PAF-PFI PS-Face Interpretation X
PAF-EFD Entity Face Description X PAF-PGI PS-Gesture Interpretation X
PAF-FIR Face Identity Recognition X PAF-PSD Personal Status Display X
PAF-FPS Face Personal Status Extraction X PAF-SPA Service Participant Authentication X
PAF-GPS Gesture Personal Status Extraction X PAF-VSC Visual Scene Creation X

2. Conformance Testing

An implementation of an AI Module conforms with MPAI-PAF V1.3 if it accepts as input and produces as output Data and/or Data Objects conforming with those specified by MPAI-PAF V1.3. Note that Data Object is defined as the combination of Data of a Data Type and its Qualifier.

The Conformance of an instance of a Data is to be expressed by a sentence like “Data validates against the Data Type Schema”. This means that:

  • Any Data Sub-Type is as indicated in the Qualifier.
  • The Data Format is indicated by the Qualifier.
  • Any File and/or Stream have the Formats indicated by the Qualifier.
  • Any Attribute of the Data is of the type or validates against the Schema specified in the Qualifier.

The method to Test the Conformance of a Data or Data Object instance is specified in the Data Types chapter.

3. Performance Assessment

Performance Assessment provides methods of assessing the performance of an AIM. Performance may have various connotations, such as:

  1. Quality: Performance Assessment measures how well an AIM performs its function, using a metric that depends on the nature of the function, e.g., how well a Face Identity Recognition (FIR) AIM identifies Faces.
  2. Bias: Performance Assessment measures the preference given by an AIM to certain elements, using a metric that depends on a bias related to certain attributes of the AIM. For instance, a Face Identity Recognition (FIR) AIM tends to have a higher correct identification of Face having a particular skin colour.
  3. Legal compliance: Performance Assessment measures how well an AIM performs its function, using a metric that assesses its accordance with a certain legal standard.

The current version V1.3 does not provide Performance Assessment methods for any AIM.

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