Personal Memory Assistant

1
Personal Memory Assistant
Abstract Facial recognition and speaker
verification systems have been widely used in the
security field. In this area the systems have to
be very accurate to prevent unauthorized users
from accessing classified information. The
extensive list of possible uses of these
technologies in the commercial world has not been
taken advantage of yet. It is often difficult to
remember the name of a person who is encountered
out of context or infrequently. This situation
can prove to be very embarrassing for the
forgetful person. It can also be insulting to
the person who is not remembered. The Personal
Memory Assistant uses facial recognition and
speaker identification to help avoid this
situation. A user discretely collects images and
voice samples of the person to be identified.
The facial recognition component analyzes the
image to identify the three closest facial
matches in the system. The speaker
identification component does the same to
identify the top two voice matches. The top
ranked IDs are compared using an algorithm that
was developed through testing. If the IDs match,
a picture of the person and personal profile is
displayed to the user. If no match is made, the
user has the option to add the subject to the
database. In addition to the identification
process, the system also gives the option of
searching for and updating entries in the
database. Group 7 Authors Scott Kyle CTE
08 Erika Sanchez EE 08 Meredith Skolnick CTE
08 Advisor Dr. Kenneth Laker University of
Pennsylvania Dept. of Electrical and Systems
Engineering
Facial Recognition System The facial
identification system is divided into two
components detection and recognition.  Detection
isolates the desired face out of an image using
the Intel Open Computer Vision library object
detection algorithm.  This algorithm uses a
trained cascade of boosted classifiers based on
Haar-like features (spatial contrasts) to
determine if a certain region of the image is a
face. The cache serves as the link between the
detection and recognition components.  It stores
sequential detected faces with fault tolerance
for false and missed detections in frames.  The
recognition component aligns the faces and masks
the background before employing an eigenface
algorithm, which is a combination of Principal
Component Analysis (PCA) and Linear Discriminant
Analysis (LDA).  The eigenfaces are the principal
sets of eigenvectors derived from covariance
matrices that are calculated from the difference
between the captured face and the mean of an
aligned set of stored faces for each person.
Speaker Identification System The speech wave
goes through the following three major processing
steps preprocessing, feature extraction and
pattern matching. The preprocessing step is
performed to normalize the amplitude of the
entire voice sample so that the signal amplitudes
vary between -1 and 1. In the Feature Extraction
process, the signal is analyzed and spectral
amplitudes are saved. A Fast Fourier Transform
is performed on the signal, and the spectrum
values are saved as the speakers unique feature
set. When appropriate features have been
extracted from the speech signal, they are
compared to the features of all the saved
signals. The method for pattern matching that is
used by this system is the Nearest Neighbor
Algorithm using Euclidean distances. Signals
processed by the system are either saved in the
population database and used as voiceprints to be
compared to future input or added to an existing
voiceprint. As more samples of the subjects
voice are saved, the system is able to improve
the voiceprint and more accurately identify this
subject.
Testing Process More than 100 people of different
races, genders, and ages were used to test the
functionality of the system. A subject pool with
demographics representative of the U.S.
population was used in order to ensure uniform
performance. Each subject was entered into the
database and then face and voice samples were
collected for three trials. All of the
similarity measurements were stored and an
algorithm analyzed the results. The comparison
formula was developed from these results to be
used for the recognition process.