Commit cbbdb801ecf953636c9cc324c4bdf6d8f502e13f
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share/openbr/likely/face_recognition.tex
| ... | ... | @@ -5,7 +5,7 @@ |
| 5 | 5 | { \verbatim } |
| 6 | 6 | { \endverbatim } |
| 7 | 7 | |
| 8 | -\title{Likely Port: Face Recognition} | |
| 8 | +\title{Face Recognition in Likely} | |
| 9 | 9 | \author{Joshua C. Klontz} |
| 10 | 10 | \date{\today} |
| 11 | 11 | \begin{document} |
| ... | ... | @@ -16,9 +16,37 @@ This document represents a long-term effort to port the OpenBR face recognition |
| 16 | 16 | As Likely is a literate programming language, this document is both the source code \emph{and} the documentation. |
| 17 | 17 | \end{abstract} |
| 18 | 18 | |
| 19 | +\section{Introduction} | |
| 20 | +We start our journey constructing the face recognition algorithm with an identity function. | |
| 21 | + | |
| 22 | +\begin{likely} | |
| 23 | +face-recognition := | |
| 24 | + src :-> | |
| 25 | + src | |
| 26 | +\end{likely} | |
| 27 | + | |
| 28 | +Throughout the remainder of this document we will append additional steps to the \texttt{face-recognition} function, building up the entire algorithm one transformation at a time. | |
| 29 | + | |
| 30 | +Note that the remainder of this document assumes that the global variable \texttt{data} is defined, and contains the appropriate training samples. | |
| 31 | + | |
| 32 | +\begin{likely} | |
| 33 | +"Num Training Samples:" | |
| 34 | +data.frames | |
| 35 | +\end{likely} | |
| 36 | + | |
| 37 | +\section{...} | |
| 38 | +This section serves as a placeholder for the unwritten sections. | |
| 39 | +There are a lot of sections that haven't been written yet, including face detection, registration and representation. | |
| 40 | +In fact, only the final section of the algorithm has been written. | |
| 41 | + | |
| 19 | 42 | \section{Quantization} |
| 43 | +The goal of quantization is to re-scale and cast feature vector dimensions into 8-bit unsigned integers. | |
| 44 | +The resulting feature vectors are smaller in size and faster to compare, with generally negligible loss in representation accuracy. | |
| 45 | + | |
| 46 | +The training data is used to determine the scaling parameters. | |
| 47 | + | |
| 20 | 48 | \begin{likely} |
| 21 | -quantize := | |
| 49 | +train-quantize := | |
| 22 | 50 | () :-> |
| 23 | 51 | { |
| 24 | 52 | lo := data.min-element |
| ... | ... | @@ -34,15 +62,14 @@ quantize := |
| 34 | 62 | } |
| 35 | 63 | \end{likely} |
| 36 | 64 | |
| 37 | -\section{Consolidated Algorithm} | |
| 38 | -The top level definition of the face recognition algorithm. | |
| 65 | +Now we can re-define \texttt{face-recognition} to include quantization. | |
| 39 | 66 | |
| 40 | 67 | \begin{likely} |
| 41 | 68 | face-recognition := |
| 42 | 69 | { |
| 43 | - algorithm := (quantize) | |
| 70 | + quantize := (train-quantize) | |
| 44 | 71 | src :-> |
| 45 | - src.algorithm | |
| 72 | + src.face-recognition.quantize | |
| 46 | 73 | } |
| 47 | 74 | \end{likely} |
| 48 | 75 | ... | ... |