How does face recognition work?

Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It utilizes computer algorithms to analyze and compare unique facial characteristics such as the distance between the eyes, the shape of the nose, and the contours of the face. Face recognition is commonly used for security, access control, and authentication purposes. It has applications in various fields, including law enforcement, surveillance, unlocking devices, and personalized user experiences. Let's see how it works.

Image acquisition

The first step in face recognition involves acquiring an image of the subject's face. This can be accomplished through various devices such as cameras, smartphones, or specialized sensors. The acquired image is represented as a digital matrix of pixel values.

Preprocessing

After image acquisition, we apply preprocessing to enhance the quality of the acquired image and make it suitable for further analysis. Common preprocessing techniques include:

• Normalization: In normalization, pixel values are adjusted to enhance lighting conditions and color balance. The image $I$ is normalized using the formula:

In the above equation, $I$ is representing the image pixels, $\mu(I)$ is the mean (average) value and $\sigma(I)$ is the standard deviation of the image $I$.

• Scaling and Cropping: The aligned face region is resized to a standard size, often pixels. This step facilitates uniformity and simplifies subsequent computations.

• Noise Reduction: A noise reduction filter, such as Gaussian blur, is applied to the normalized image.

Face detection

Face detection involves identifying the location of a face within an image. One popular approach is the Viola-Jones algorithm, which uses Haar-like features and a cascade of classifiers. The algorithm calculates a feature value for each Haar-like feature and applies a series of classifiers to determine if a face is present.

Face representation

After detecting the faces, we need to flatten the representation of the detected face. Suppose the detected face is stored in matrix $F$, a submatrix of image $I$. $F$will be 2-dimensional, let's say it is $n$ by $m$. We will flatten this by concatenating all the rows, representing it in one dimension of length $nm$.

The dataset of images will also be stored as a 2-d matrix, with each row representing a flattened image.

Face matching and recognition

Feature extraction involves capturing distinctive facial attributes that aid in differentiation between individuals. One popular method is Principal Component Analysis (PCA).

PCA reduces the dimensionality of the data while retaining as much variance as possible. Given a set of face images $M$, the covariance matrix is computed as:

Where $M$ is the matrix of face image data. The eigenvectors and eigenvalues of $C$ are calculated using PCA.

Similarity calculation

After we get the eigenfaces representation, we can find the similarity of the input faces with all the faces of the dataset. One way is to calculate the euclidean distance between the input and all the images of the dataset. Then, we can find the similarity of each image as:

A smaller Euclidean distance indicates a higher similarity between the two faces. Faces with smaller Euclidean distances are more likely to be similar to each other.

Decision-making

Decisions are based on similarity scores. For identity verification, a similarity threshold is defined. If the similarity score surpasses the threshold, the subject is recognized:

In identification scenarios, the most similar identity is determined by selecting the identity that has the highest similarity score:

Note: For python implementation of face recognition, you can read: Face recognition in opencv

Conclusion

Face recognition technology uses advanced algorithms to transform the detailed features of a person's face into mathematically understood patterns, which are then compared to a database of stored patterns for identification or verification. This seamless integration of computer vision and pattern recognition has led to transformative applications across industries, improving security, convenience, and user experiences.