Is Viola-Jones algorithm still used?

Therefore, while CNNs are faster and much more reliable in terms of accuracy, the Viola-Jones algorithm is still widely used today. Two modern products using the Viola-Jones algorithm are various models of point-and-shoot digital cameras and the social media app Snapchat (Q, 2017).

Why Viola-Jones algorithm is used?

Viola-Jones was designed for frontal faces, so it is able to detect frontal the best rather than faces looking sideways, upwards or downwards. Before detecting a face, the image is converted into grayscale, since it is easier to work with and there’s lesser data to process.

Does OpenCV use Viola-Jones?

Using a Viola-Jones Classifier. Training a Viola-Jones classifier from scratch can take a long time. Fortunately, a pre-trained Viola-Jones classifier comes out-of-the-box with OpenCV! You will use that one to see the algorithm in action.

How do you implement Viola-Jones?


  1. Initialize the weights.
  2. Normalize the weights.
  3. Select the best weak classifier (based on the weighted error)
  4. Update the weights based on the chosen classifiers error.
  5. Repeat steps 2–4 T times where T is the desired number of weak classifiers.

Is Viola Jones deep learning?

It is concerned with detecting instances of an object such as human faces, buildings, trees, cars, etc. Although there are quite advanced face detection algorithms, especially with the introduction of deep learning, the introduction of viola jones algorithm in 2001 was a breakthrough in this field.

What is the best face detection algorithm?

MTCNN or Multi-Task Cascaded Convolutional Neural Network is unquestionably one of the most popular and most accurate face detection tools today.

Is Viola Jones a deep learning algorithm?

Back in 2011, before Convolutional Neural Networks or any other type of Deep Learning techniques were in vogue, there was the Viola-Jones algorithm. This algorithm was so innovative and effective that the paper was citied over 19,000 times.

What is LBPH algorithm?

LBPH (Local Binary Pattern Histogram) is a Face-Recognition algorithm it is used to recognize the face of a person. It is known for its performance and how it is able to recognize the face of a person from both front face and side face.

What is Fisherface algorithm?

Fisherfaces algorithm extracts principle components that separates one individual from another. So , now an individual’s features can’t dominate another person’s features. LDA is used to find a linear combination of features that separates two or more classes or objects.

Which algorithm is best for face recognition?

Conclusions. LBPH is one of the easiest face recognition algorithms. It can represent local features in the images. It is possible to get great results (mainly in a controlled environment).

How does the Viola Jones face detector work?

In order for the detector to be fast, we should quickly reject windows without faces. Viola-Jones detector combines four key ideas, the simple Haar features, the use of integral images for fast feature computation, boosting for feature selection and the main thing, the attentional cascade for fast rejection of windows without faces.

When was the Viola-Jones object detection framework created?

Developed by Paul Viola and M i chael Jones back in 2001, the Viola-Jones Object Detection Framework can quickly and accurately detect objects in images and works particularly well with the human face (Viola & Jones, 2001). Despite its age, the framework is still a leading player in face detection along side many of its CNNs counter parts.

Is the Viola-Jones algorithm a good detection algorithm?

While it seems these constraints could diminish the algorithm’s utility somewhat, because the detection step is most often followed by a recognition step, in practice these limits on pose are quite acceptable. The characteristics of Viola–Jones algorithm which make it a good detection algorithm are:

How big should the viola and Jones image be?

Viola & Jones (2001) recommends the images to be 24 x 24. Since each type of Haar-like features can have different sizes and positions in a 24 x 24 window, over 160,000 Haar-like features can be extracted.