diff --git a/doc/struct_bio_rec_sys.rst b/doc/struct_bio_rec_sys.rst
index f8e555ba20b8ada8039938d4ffdca88e809a84ea..61b30aa6db08f0e1179851e0f54a071c9b2c75a8 100644
--- a/doc/struct_bio_rec_sys.rst
+++ b/doc/struct_bio_rec_sys.rst
@@ -7,9 +7,9 @@ Structure of a Biometric Recognition System
This section will familiarize you with the structure of a typical biometric recognition system to help you understand and use the ``bob.bio`` framework to set up your own biometric recognition experiments.
"Biometric recognition" refers to the process of establishing a person's identity based on their biometric data.
-A biometric recognition system can operate in one of two modes: *verification* or *identification*.
+A biometric recognition system can operate in one of two modes: *verification* or *identification*.
A *verification* system establishes whether or not a person is who they say they are (i.e., the person claims an identity and the system tries to prove whether or not that claim is true).
-On the other hand, an *identification* system attempts to establish a person's identity from scratch (i.e., the system tries to associate a person with an identity from a set of identities in the system's database). When we are talking about neither verification nor identification in particular, the generic term *recognition* is used.
+On the other hand, an *identification* system attempts to establish a person's identity from scratch (i.e., the system tries to associate a person with an identity from a set of identities in the system's database). When we are talking about neither verification nor identification in particular, the generic term *recognition* is used.
A biometric recognition system has two stages:
@@ -31,6 +31,7 @@ Fig. 2 shows the enrollment and identification stages in a typical biometric *id
Enrollment and identification in a typical biometric identification system.
In the figures above:
+
* The "Pre-processor" cleans up the raw biometric data to make recognition easier (e.g., crops the face image to get rid of the background).
* The "Feature Extractor" extracts the most important features for recognition, from the pre-processed biometric data.
* The "Model Database" stores each person's extracted feature set in the form of a representative model for that person in the system database, typically alongside the person's ID.
@@ -45,7 +46,7 @@ The ``bob.bio`` framework has the capability to perform both *verification* and
In general, the goal of a biometric recognition experiment is to quantify the recognition accuracy of a biometric recognition system, e.g., we wish to find out how good the system is at deciding whether or not two biometric samples come from the same person.
-To conduct a biometric recognition experiment, we need biometric data. So, we use a biometric database. A biometric database generally consists of multiple samples of a particular biometric, from multiple people. For example, a face database could contain 5 different images of a person's face, from 100 people. The dataset is split up into samples used for enrollment, and samples used for probing. We then simulate "genuine" recognition attempts by comparing each person's probe samples to their enrolled models. We simulate "impostor" recognition attempts by comparing the same probe samples to models of different people.
+To conduct a biometric recognition experiment, we need biometric data. So, we use a biometric database. A biometric database generally consists of multiple samples of a particular biometric, from multiple people. For example, a face database could contain 5 different images of a person's face, from 100 people. The dataset is split up into samples used for enrollment, and samples used for probing. We enroll a model for each identity from one or more of its faces. We then simulate "genuine" recognition attempts by comparing each person's probe samples to their enrolled models. We simulate "impostor" recognition attempts by comparing the same probe samples to models of different people.
In ``bob.bio``, biometric recognition experiments are split up into four main stages, similar to the stages in a typical verification or identification system as illustrated in Fig. 1 and Fig. 2, respectively:
@@ -97,7 +98,7 @@ The matching stage in ``bob.bio`` is referred to as the "Algorithm". The Algori
The projection part of the Algorithm stage in ``bob.bio``'s biometric recognition experiment framework.
-(ii) Enrollment: The enrollment part of the Algorithm stage essentially works as follows. One or more biometric samples per person is used to compute a representative "model" for that person, which essentially represents that person's identity. To determine which of a person's biometric samples should be used to generate their model, we query our input biometric database. The model is then calculated using the corresponding biometric features extracted in the Feature Extraction stage (or, optionally, our "projected" features). Fig. 6 illustrates the enrollment part of the Algorithm module:
+(ii) Enrollment: The enrollment part of the Algorithm stage essentially works as follows. One or more biometric samples per person are used to compute a representative "model" for that person, which essentially represents that person's identity. To determine which of a person's biometric samples should be used to generate their model, we query the protocol of our input biometric database. The model is then calculated using the corresponding biometric features extracted in the Feature Extraction stage (or, optionally, our "projected" features). Fig. 6 illustrates the enrollment part of the Algorithm module:
.. figure:: /img/algorithm_enrollment.svg
:align: center
@@ -118,7 +119,7 @@ Note that there is sometimes a model enroller training stage prior to enrollment
Decision Making:
~~~~~~~~~~~~~~~~
-The decision making stage in ``bob.bio`` is referred to as "Evaluation". If we wish to perform *verification*, then the aim of this stage will be to make a decision as to whether each score calculated in the Matching stage indicates a "Match" or "No Match" between the particular model and probe biometrics. If we wish to perform *identification*, then the aim of the evaluation stage will be to find the model which most closely matches the probe biometric.
+The decision making stage in ``bob.bio`` is referred to as "Evaluation". If we wish to perform *verification*, then the aim of this stage will be to make a decision as to whether each score calculated in the Matching stage indicates a "Match" or "No Match" between the particular model and probe biometrics. If we wish to perform *identification*, then the aim of the evaluation stage will be to find the model which most closely matches the probe biometric.
Once a decision has been made, we can quantify the overall performance of the particular biometric recognition system in terms of common metrics like the False Match Rate (FMR), False Non Match Rate (FNMR), and Equal Error Rate (EER) for verification, and Identification Rate (IR) for identification. We can also view a visual representation of the performance in terms of plots like the Receiver Operating Characteristic (ROC) and Detection Error Trade-off (DET) for verification, Cumulative Match Characteristics (CMC) for closed-set identification, and Detection and Identification Rate (DIR) for open-set identification. Fig. 7 illustrates the Evaluation stage:
@@ -130,9 +131,9 @@ Once a decision has been made, we can quantify the overall performance of the pa
.. note::
- * The "Data Preprocessing" to "Matching" steps are carried out by ``bob.bio.base``s ``verify.py`` script. The "Decision Making" step is carried out by ``bob.bio.base``'s ``evaluate.py`` script. These scripts will be discussed in the next sections.
+ * The "Data Preprocessing" to "Matching" steps are carried out by ``bob.bio.base``'s ``verify.py`` script. The "Decision Making" step is carried out by ``bob.bio.base``'s ``evaluate.py`` script. These scripts will be discussed in the next sections.
* The communication between any two steps in the recognition framework is file-based, usually using a binary HDF5_ interface, which is implemented, for example, in the :py:class:`bob.io.base.HDF5File` class.
* The output of one step usually serves as the input of the subsequent step(s), as portrayed in Fig. 3 -- Fig. 7.
* ``bob.bio`` ensures that the correct files are always forwarded to the subsequent steps. For example, if you choose to implement a feature projection after the feature extraction stage, as illustrated in Fig. 5, ``bob.bio`` will make sure that the files in the "projected" directory are passed on as the input to the Enrollment stage; otherwise, the "extracted" directory will become the input to the Enrollment stage.
-.. include:: links.rst
\ No newline at end of file
+.. include:: links.rst