Systems and methods for automatic detection of human expression from multimedia content

A method for automatically detecting a statement's veracity, the method comprising:

• receiving a multimedia file comprising an interview of one or more participants of interest and one or more file components, the one or more file components comprising one or more of a transcript file, an audio file, and a video file;
• determining, from a user input, a desired characteristic;
• segmenting the multimedia file into at least one chunk;
• identifying, from the at least one chunk, an expression, the expression comprising any of a facial expression characteristics, vocal characteristics, and textual characteristics from multimedia content;
• generating from the expression and via a multi-tier classification algorithm, a score related to one or more of a plurality of statements, the score indicating a likelihood of the desired characteristic being present, wherein the multi-tier classification algorithm is a machine learning algorithm, the machine learning algorithm including a computer-implemented method comprising:
• receiving an input dataset comprising at least any of a facial expression characteristics, vocal characteristics, and textual characteristics from the at least one chunk;
• determining a first tier determination comprising a first confidence level of a presence of the characteristic;
• determining a second tier determination comprising a second confidence level of a lack of the characteristic;
• determining a third tier determination comprising a third confidence level of both the presence and the lack of the characteristic;
• producing an output dataset comprising the score for the at least one chunk based on the first confidence level, the second confidence level, and the third confidence level;
• assigning the score to the corresponding at least one chunk;
• and
• displaying a user interface configured to display any of audio, text, or video components and the score associated with the at least one chunk.

At least one non-transitory computer-readable medium comprising a plurality of instructions that, when executed by at least one processor, are configured to:

• receive a multimedia file comprising an interview of one or more participants of interest and one or more file components, the one or more file components comprising one or more of a transcript file, an audio file, and a video file;
• determine, from a user input, a desired characteristic;
• segment the multimedia file into at least one chunk;
• identify, from the at least one chunk, an expression, the expression comprising any of a facial expression characteristics, vocal characteristics, and textual characteristics from multimedia content;
• generate from the expression and via a multi-tier classification algorithm, a score related to one or more of a plurality of statements, the score indicating a likelihood of the desired characteristic being present, wherein the multi-tier classification algorithm is a machine learning algorithm, the machine learning algorithm including a computer-implemented method comprising:
• receiving an input dataset comprising at least any of a facial expression characteristics, vocal characteristics, and textual characteristics from the at least one chunk;
• determining a first tier determination comprising a first confidence level of a presence of the characteristic;
• determining a second tier determination comprising a second confidence level of a lack of the characteristic;
• determining a third tier determination comprising a third confidence level of both the presence and the lack of the characteristic;
• producing an output dataset comprising the score for the at least one chunk based on the first confidence level, the second confidence level, and the third confidence level;
• assign the score to the corresponding at least one chunk;
• and
• display a user interface configured to display any of audio, text, or video components and the score associated with the at least one chunk.